modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-06 00:36:47
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 540
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-06 00:36:27
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1757079300
|
helmutsukocok
| 2025-09-05T14:00:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:00:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kiddiszc/Qwen2.5-1B-Instruct-Gensyn-Swarm-vocal_lithe_flea
|
kiddiszc
| 2025-09-05T12:26:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am vocal_lithe_flea",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T05:45:59Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am vocal_lithe_flea
---
# 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]
|
bah63843/blockassist-bc-plump_fast_antelope_1757074995
|
bah63843
| 2025-09-05T12:24:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T12:23:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Navid-AI/Yehia-7B-preview
|
Navid-AI
| 2025-09-05T12:22:21Z | 5,790 | 21 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ar",
"en",
"base_model:ALLaM-AI/ALLaM-7B-Instruct-preview",
"base_model:finetune:ALLaM-AI/ALLaM-7B-Instruct-preview",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-02-27T19:50:53Z |
---
language:
- ar
- en
base_model:
- ALLaM-AI/ALLaM-7B-Instruct-preview
pipeline_tag: text-generation
library_name: transformers
license: apache-2.0
---
# Yehia: A Simple (nice to talk to) Arabic Model
<center>
<img src="https://cdn-uploads.huggingface.co/production/uploads/6116d0584ef9fdfbf45dc4d9/1OUwFm2hWBAHLCVvh2JkG.png" width="75%">
</center>
## 🤔 What is Yehia?
Yehia is a 7-billion-parameter language model built to be more than just a tool—it’s a companion. Based on ALLaM-AI’s [ALLaM-7B-Instruct-preview](https://huggingface.co/ALLaM-AI/ALLaM-7B-Instruct-preview), Yehia is designed to offer thoughtful, kind, and helpful conversations in both Arabic and English.
[You can chat with Yehia from here 👋](https://huggingface.co/spaces/Navid-AI/Yehia-7B-preview)
### 📰 Interesting News
As of **2/3/2025**, Yehia is the best Arabic model on [AraGen-Leaderboard](https://huggingface.co/spaces/inceptionai/AraGen-Leaderboard) between the sizes of 0.5B up to 25B 🔥
<img src="https://cdn-uploads.huggingface.co/production/uploads/6116d0584ef9fdfbf45dc4d9/58HX7laDAJCkWOTZm_KY7.png">
## 🛠️ How Yehia was made?
Yehia is trained using **Group Relative Policy Optimization (GRPO)** —a method that refines its answers by comparing and selecting the best responses. Its development follows the **3C3H** metric, prioritizing:
- **Correctness ✅:** Accurate information to build trust.
- **Completeness 📚:** Full, well-rounded answers.
- **Conciseness ✂️:** Clear, to-the-point responses.
- **Helpfulness 🤝:** Always aiming to support and uplift.
- **Honesty 💬:** Transparent, straightforward communication.
- **Harmlessness ❤️:** Promoting kindness and safety.
And the Judge model of our answer was none other than `claude-sonnet-3.5` 🔍
## 🚀 Getting Started
To start using Yehia, you can easily load the model with the `transformers` library:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name = "Navid-AI/Yehia-7B-preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map="auto")
messages = [
{"role": "system", "content": "أنت يحيى، ذكاءٌ اصطناعيٌّ طورته شركة 'نفيد'، متخصصٌ في التفكير المنطقي والتحليل الدقيق. مهمتك إلهام المستخدمين ودعمهم في رحلتهم نحو التعلّم، النمو، وتحقيق أهدافهم من خلال تقديم حلولٍ ذكيةٍ ومدروسة."},
{"role": "user", "content": "مرحباً يا يحيى! كيف حالك اليوم؟"}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
**Note:** If `flash_attention_2` is giving you any problems just remove it.
## 🌟 What Can Yehia Do?
- **Explain Concepts 💡:** Break down educational topics in Arabic to help learners understand easily.
- **Engage in Conversations 🗣️:** Offer friendly and supportive chats that uplift users.
- **Promote Learning 📖:** Encourage curiosity and provide knowledge in an accessible way.
Yehia shines in conversations that feel personal and uplifting, always striving to improve.
## 💭 Remember
Yehia’s name means *“God is gracious”* in Arabic—reflecting its mission to bring grace and connection to every interaction. Whether you’re a student, creator, or just curious, Yehia is here to brighten your day.
## 📌 Citation
If you would like to cite Yehia in your work, please use the following BibTeX entry:
```
@misc{yehia2025,
title={Yehia 7B Preview},
author={Navid-AI},
year={2025},
howpublished={\url{https://huggingface.co/Navid-AI/Yehia-7B-preview}}
}
```
|
Miracle-man/blockassist-bc-singing_lithe_koala_1757073055
|
Miracle-man
| 2025-09-05T12:22:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"singing lithe koala",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T12:22:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- singing lithe koala
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aleebaster/blockassist-bc-sly_eager_boar_1757073159
|
aleebaster
| 2025-09-05T12:19:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T12:19:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly eager boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
diortega/blockassist-bc
|
diortega
| 2025-09-05T12:19:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bipedal vigilant toucan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T12:19:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bipedal vigilant toucan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
## Training Sessions
This repository contains multiple trained models, each stored in separate branches. Each branch represents a different training session.
Browse the branches to see different training runs and their associated models.
|
vommertou/blockassist-bc-mute_whistling_hamster_1757074719
|
vommertou
| 2025-09-05T12:19:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute whistling hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T12:18:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute whistling hamster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vommertou/blockassist-bc-colorful_smooth_elk_1757074411
|
vommertou
| 2025-09-05T12:13:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful smooth elk",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T12:13:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful smooth elk
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tehtelur666/indobert-semeval
|
tehtelur666
| 2025-09-05T12:09:42Z | 0 | 0 | null |
[
"base_model:indobenchmark/indobert-base-p1",
"base_model:finetune:indobenchmark/indobert-base-p1",
"license:apache-2.0",
"region:us"
] | null | 2025-09-05T11:53:39Z |
---
license: apache-2.0
base_model:
- indobenchmark/indobert-base-p1
---
|
dsagasdgds/blockassist-bc-unseen_camouflaged_komodo_1757072787
|
dsagasdgds
| 2025-09-05T12:07:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"unseen camouflaged komodo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T12:07:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- unseen camouflaged komodo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
DiFors/blockassist-bc-singing_sizable_snake_1757073894
|
DiFors
| 2025-09-05T12:05:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"singing sizable snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T12:05:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- singing sizable snake
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
NahedDom/blockassist-bc-flapping_stocky_leopard_1757071016
|
NahedDom
| 2025-09-05T11:54:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:54:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping stocky leopard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sahildo/blockassist-bc-sizable_lanky_owl_1757073110
|
Sahildo
| 2025-09-05T11:52:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sizable lanky owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:52:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sizable lanky owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Miracle-man/blockassist-bc-singing_lithe_koala_1757070942
|
Miracle-man
| 2025-09-05T11:45:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"singing lithe koala",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:45:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- singing lithe koala
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
DapaoZeng/ddpm-celebahq-finetuned-butterflies-2epochs
|
DapaoZeng
| 2025-09-05T11:44:41Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2025-09-05T11:44:29Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('DapaoZeng/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
knightluffy/qwen34bfine1
|
knightluffy
| 2025-09-05T11:43:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2025-09-05T07:08:14Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** knightluffy
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-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)
|
giovannidemuri/llama3b-llama8b-er-v585-seed2-seed2-hx-openmath-fpt
|
giovannidemuri
| 2025-09-05T11:39:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T10:08:13Z |
---
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]
|
mradermacher/Clowncar-dev-v3-RP-ERP-pre-training-v0.2-GGUF
|
mradermacher
| 2025-09-05T11:36:25Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:SuperbEmphasis/Clowncar-dev-v3-RP-ERP-pre-training-v0.2",
"base_model:quantized:SuperbEmphasis/Clowncar-dev-v3-RP-ERP-pre-training-v0.2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-05T10:23:30Z |
---
base_model: SuperbEmphasis/Clowncar-dev-v3-RP-ERP-pre-training-v0.2
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/SuperbEmphasis/Clowncar-dev-v3-RP-ERP-pre-training-v0.2
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Clowncar-dev-v3-RP-ERP-pre-training-v0.2-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Clowncar-dev-v3-RP-ERP-pre-training-v0.2-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Clowncar-dev-v3-RP-ERP-pre-training-v0.2-GGUF/resolve/main/Clowncar-dev-v3-RP-ERP-pre-training-v0.2.Q2_K.gguf) | Q2_K | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/Clowncar-dev-v3-RP-ERP-pre-training-v0.2-GGUF/resolve/main/Clowncar-dev-v3-RP-ERP-pre-training-v0.2.Q3_K_S.gguf) | Q3_K_S | 17.0 | |
| [GGUF](https://huggingface.co/mradermacher/Clowncar-dev-v3-RP-ERP-pre-training-v0.2-GGUF/resolve/main/Clowncar-dev-v3-RP-ERP-pre-training-v0.2.Q3_K_M.gguf) | Q3_K_M | 18.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Clowncar-dev-v3-RP-ERP-pre-training-v0.2-GGUF/resolve/main/Clowncar-dev-v3-RP-ERP-pre-training-v0.2.Q3_K_L.gguf) | Q3_K_L | 20.3 | |
| [GGUF](https://huggingface.co/mradermacher/Clowncar-dev-v3-RP-ERP-pre-training-v0.2-GGUF/resolve/main/Clowncar-dev-v3-RP-ERP-pre-training-v0.2.IQ4_XS.gguf) | IQ4_XS | 21.1 | |
| [GGUF](https://huggingface.co/mradermacher/Clowncar-dev-v3-RP-ERP-pre-training-v0.2-GGUF/resolve/main/Clowncar-dev-v3-RP-ERP-pre-training-v0.2.Q4_K_S.gguf) | Q4_K_S | 22.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Clowncar-dev-v3-RP-ERP-pre-training-v0.2-GGUF/resolve/main/Clowncar-dev-v3-RP-ERP-pre-training-v0.2.Q4_K_M.gguf) | Q4_K_M | 23.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Clowncar-dev-v3-RP-ERP-pre-training-v0.2-GGUF/resolve/main/Clowncar-dev-v3-RP-ERP-pre-training-v0.2.Q5_K_S.gguf) | Q5_K_S | 26.8 | |
| [GGUF](https://huggingface.co/mradermacher/Clowncar-dev-v3-RP-ERP-pre-training-v0.2-GGUF/resolve/main/Clowncar-dev-v3-RP-ERP-pre-training-v0.2.Q5_K_M.gguf) | Q5_K_M | 27.6 | |
| [GGUF](https://huggingface.co/mradermacher/Clowncar-dev-v3-RP-ERP-pre-training-v0.2-GGUF/resolve/main/Clowncar-dev-v3-RP-ERP-pre-training-v0.2.Q6_K.gguf) | Q6_K | 31.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Clowncar-dev-v3-RP-ERP-pre-training-v0.2-GGUF/resolve/main/Clowncar-dev-v3-RP-ERP-pre-training-v0.2.Q8_0.gguf) | Q8_0 | 41.2 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Persona-V1-70B-GGUF
|
mradermacher
| 2025-09-05T11:36:21Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:TareksLab/Persona-V1-70B",
"base_model:quantized:TareksLab/Persona-V1-70B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-05T10:32:47Z |
---
base_model: TareksLab/Persona-V1-70B
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/TareksLab/Persona-V1-70B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Persona-V1-70B-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Persona-V1-70B-GGUF/resolve/main/Persona-V1-70B.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Persona-V1-70B-GGUF/resolve/main/Persona-V1-70B.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Persona-V1-70B-GGUF/resolve/main/Persona-V1-70B.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Persona-V1-70B-GGUF/resolve/main/Persona-V1-70B.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Persona-V1-70B-GGUF/resolve/main/Persona-V1-70B.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/Persona-V1-70B-GGUF/resolve/main/Persona-V1-70B.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Persona-V1-70B-GGUF/resolve/main/Persona-V1-70B.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Persona-V1-70B-GGUF/resolve/main/Persona-V1-70B.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Persona-V1-70B-GGUF/resolve/main/Persona-V1-70B.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Persona-V1-70B-GGUF/resolve/main/Persona-V1-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Persona-V1-70B-GGUF/resolve/main/Persona-V1-70B.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Persona-V1-70B-GGUF/resolve/main/Persona-V1-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Persona-V1-70B-GGUF/resolve/main/Persona-V1-70B.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
ASLP-lab/Cosyvoice2-Yue-ZoengJyutGaai
|
ASLP-lab
| 2025-09-05T11:32:40Z | 0 | 0 | null |
[
"onnx",
"safetensors",
"arxiv:2509.03959",
"region:us"
] | null | 2025-08-25T04:04:35Z |
## 👉🏻 WenetSpeech-Yue 👈🏻
**WenetSpeech-Yue**: [Demos](https://aslp-lab.github.io/WenetSpeech-Yue/); [Paper](https://arxiv.org/abs/2509.03959); [Github](https://github.com/ASLP-lab/WenetSpeech-Yue); [HuggingFace](https://huggingface.co/datasets/ASLP-lab/WenetSpeech-Yue)
## Highlight🔥
**WenetSpeech-Yue TTS Models** have been released!
This repository contains two versions of the TTS models:
1. **ASLP-lab/Cosyvoice2-Yue**: The base model for Cantonese TTS.
2. **ASLP-lab/Cosyvoice2-Yue-ZoengJyutGaai**: A fine-tuned, higher-quality version for more natural speech generation.
## Roadmap
- [x] 2025/9
- [x] 25hz WenetSpeech-Yue TTS models released
## Install
**Clone and install**
- Clone the repo
``` sh
git clone --recursive https://github.com/FunAudioLLM/CosyVoice.git
# If you failed to clone submodule due to network failures, please run following command until success
cd CosyVoice
git submodule update --init --recursive
```
- Install Conda: please see https://docs.conda.io/en/latest/miniconda.html
- Create Conda env:
``` sh
conda create -n cosyvoice python=3.10
conda activate cosyvoice
# pynini is required by WeTextProcessing, use conda to install it as it can be executed on all platform.
conda install -y -c conda-forge pynini==2.1.5
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com
# If you encounter sox compatibility issues
# ubuntu
sudo apt-get install sox libsox-dev
# centos
sudo yum install sox sox-devel
```
**Model download**
1. [Cosyvoice2-Yue](https://huggingface.co/ASLP-lab/Cosyvoice2-Yue)
2. [Cosyvoice2-Yue-ZoengJyutGaai](https://huggingface.co/ASLP-lab/Cosyvoice2-Yue-ZoengJyutGaai)
**Basic Usage**
We strongly recommend using `CosyVoice2-0.5B` for better performance.
Follow code below for detailed usage of each model.
``` python
import sys
sys.path.append('third_party/Matcha-TTS')
from cosyvoice.cli.cosyvoice import CosyVoice, CosyVoice2
from cosyvoice.utils.file_utils import load_wav
import torchaudio
```
**CosyVoice2 Usage**
```python
cosyvoice = CosyVoice2('ASLP-lab/Cosyvoice2-Yue-ZoengJyutGaai', load_jit=False, load_trt=False, fp16=False)
# NOTE if you want to reproduce the results on https://funaudiollm.github.io/cosyvoice2, please add text_frontend=False during inference
# zero_shot usage
prompt_speech_16k = load_wav('zero_shot_prompt.wav', 16000)
# instruct usage
for i, j in enumerate(cosyvoice.inference_instruct2('收到朋友从远方寄嚟嘅生日礼物，嗰份意外嘅惊喜同埋深深嘅祝福令我心入面充满咗甜蜜嘅快乐，笑容好似花咁绽放。', '用粤语说这句话', prompt_speech_16k, stream=False)):
torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
```
## Contact
If you are interested in leaving a message to our research team, feel free to email [email protected] or [email protected].
|
ncgc0incendiary/statichh-pythia-2.8b-dpo-bf16
|
ncgc0incendiary
| 2025-09-05T11:30:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"dpo",
"trl",
"arxiv:2305.18290",
"base_model:ncgc/statichh-pythia-2.8b-sft-bf16",
"base_model:finetune:ncgc/statichh-pythia-2.8b-sft-bf16",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T02:49:46Z |
---
base_model: ncgc/statichh-pythia-2.8b-sft-bf16
library_name: transformers
model_name: statichh-pythia-2.8b-dpo-bf16
tags:
- generated_from_trainer
- dpo
- trl
licence: license
---
# Model Card for statichh-pythia-2.8b-dpo-bf16
This model is a fine-tuned version of [ncgc/statichh-pythia-2.8b-sft-bf16](https://huggingface.co/ncgc/statichh-pythia-2.8b-sft-bf16).
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="ncgc0incendiary/statichh-pythia-2.8b-dpo-bf16", 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/2this0username0isnt2allowed-indian-institute-of-science/huggingface/runs/bszhkihs)
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: 0.19.1
- Transformers: 4.52.4
- Pytorch: 2.8.0a0+gite2f9759
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
deepseek-ai/DeepSeek-V3.1
|
deepseek-ai
| 2025-09-05T11:30:15Z | 117,231 | 709 |
transformers
|
[
"transformers",
"safetensors",
"deepseek_v3",
"text-generation",
"conversational",
"custom_code",
"arxiv:2412.19437",
"base_model:deepseek-ai/DeepSeek-V3.1-Base",
"base_model:quantized:deepseek-ai/DeepSeek-V3.1-Base",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"fp8",
"region:us"
] |
text-generation
| 2025-08-21T02:37:52Z |
---
license: mit
library_name: transformers
base_model:
- deepseek-ai/DeepSeek-V3.1-Base
---
# DeepSeek-V3.1
<!-- 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
DeepSeek-V3.1 is a hybrid model that supports both thinking mode and non-thinking mode. Compared to the previous version, this upgrade brings improvements in multiple aspects:
- **Hybrid thinking mode**: One model supports both thinking mode and non-thinking mode by changing the chat template.
- **Smarter tool calling**: Through post-training optimization, the model's performance in tool usage and agent tasks has significantly improved.
- **Higher thinking efficiency**: DeepSeek-V3.1-Think achieves comparable answer quality to DeepSeek-R1-0528, while responding more quickly.
DeepSeek-V3.1 is post-trained on the top of DeepSeek-V3.1-Base, which is built upon the original V3 base checkpoint through a two-phase long context extension approach, following the methodology outlined in the original DeepSeek-V3 report. We have expanded our dataset by collecting additional long documents and substantially extending both training phases. The 32K extension phase has been increased 10-fold to 630B tokens, while the 128K extension phase has been extended by 3.3x to 209B tokens.
Additionally, DeepSeek-V3.1 is trained using the **UE8M0 FP8 scale data format on both model weights and activations** to ensure compatibility with microscaling data formats. Please refer to [DeepGEMM](https://github.com/deepseek-ai/DeepGEMM) for more details.
## Model Downloads
<div align="center">
| **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** |
| :------------: | :------------: | :------------: | :------------: | :------------: |
| DeepSeek-V3.1-Base | 671B | 37B | 128K | [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Base) \| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V3.1-Base) |
| DeepSeek-V3.1 | 671B | 37B | 128K | [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3.1) \| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V3.1) |
</div>
## Chat Template
The details of our chat template is described in `tokenizer_config.json` and `assets/chat_template.jinja`. Here is a brief description.
### Non-Thinking
#### First-Turn
Prefix:
`<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜></think>`
With the given prefix, DeepSeek V3.1 generates responses to queries in non-thinking mode. Unlike DeepSeek V3, it introduces an additional token `</think>`.
#### Multi-Turn
Context:
`<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>...<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>`
Prefix:
`<｜User｜>{query}<｜Assistant｜></think>`
By concatenating the context and the prefix, we obtain the correct prompt for the query.
### Thinking
#### First-Turn
Prefix:
`<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜><think>`
The prefix of thinking mode is similar to DeepSeek-R1.
#### Multi-Turn
Context:
`<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>...<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>`
Prefix:
`<｜User｜>{query}<｜Assistant｜><think>`
The multi-turn template is the same with non-thinking multi-turn chat template. It means the thinking token in the last turn will be dropped but the `</think>` is retained in every turn of context.
### ToolCall
Toolcall is supported in non-thinking mode. The format is:
`<｜begin▁of▁sentence｜>{system prompt}\n\n{tool_description}<｜User｜>{query}<｜Assistant｜></think>` where the tool_description is
```
## Tools
You have access to the following tools:
### {tool_name1}
Description: {description}
Parameters: {json.dumps(parameters)}
IMPORTANT: ALWAYS adhere to this exact format for tool use:
<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>tool_call_name<｜tool▁sep｜>tool_call_arguments<｜tool▁call▁end｜>{additional_tool_calls}<｜tool▁calls▁end｜>
Where:
- `tool_call_name` must be an exact match to one of the available tools
- `tool_call_arguments` must be valid JSON that strictly follows the tool's Parameters Schema
- For multiple tool calls, chain them directly without separators or spaces
```
### Code-Agent
We support various code agent frameworks. Please refer to the above toolcall format to create your own code agents. An example is shown in `assets/code_agent_trajectory.html`.
### Search-Agent
We design a specific format for searching toolcall in thinking mode, to support search agent.
For complex questions that require accessing external or up-to-date information, DeepSeek-V3.1 can leverage a user-provided search tool through a multi-turn tool-calling process.
Please refer to the `assets/search_tool_trajectory.html` and `assets/search_python_tool_trajectory.html` for the detailed template.
## Evaluation
| Category | Benchmark (Metric) | DeepSeek V3.1-NonThinking | DeepSeek V3 0324 | DeepSeek V3.1-Thinking | DeepSeek R1 0528
|----------|----------------------------------|-----------------|---|---|---|
| General |
| | MMLU-Redux (EM) | 91.8 | 90.5 | 93.7 | 93.4
| | MMLU-Pro (EM) | 83.7 | 81.2 | 84.8 | 85.0
| | GPQA-Diamond (Pass@1) | 74.9 | 68.4 | 80.1 | 81.0
| | Humanity's Last Exam (Pass@1) | - | - | 15.9 | 17.7
|Search Agent|
| | BrowseComp | - | - | 30.0 | 8.9
| | BrowseComp_zh | - | - | 49.2 | 35.7
| | Humanity's Last Exam (Python + Search) |- | - | 29.8 | 24.8
| | SimpleQA | - | - | 93.4 | 92.3
| Code |
| | LiveCodeBench (2408-2505) (Pass@1) | 56.4 | 43.0 | 74.8 | 73.3
| | Codeforces-Div1 (Rating) | - | - | 2091 | 1930
| | Aider-Polyglot (Acc.) | 68.4 | 55.1 | 76.3 | 71.6
| Code Agent|
| | SWE Verified (Agent mode) | 66.0 | 45.4 | - | 44.6
| | SWE-bench Multilingual (Agent mode) | 54.5 | 29.3 | - | 30.5
| | Terminal-bench (Terminus 1 framework) | 31.3 | 13.3 | - | 5.7
| Math |
| | AIME 2024 (Pass@1) | 66.3 | 59.4 | 93.1 | 91.4
| | AIME 2025 (Pass@1) | 49.8 | 51.3 | 88.4 | 87.5
| | HMMT 2025 (Pass@1) | 33.5 | 29.2 | 84.2 | 79.4 |
Note:
- Search agents are evaluated with our internal search framework, which uses a commercial search API + webpage filter + 128K context window. Seach agent results of R1-0528 are evaluated with a pre-defined workflow.
- SWE-bench is evaluated with our internal code agent framework.
- HLE is evaluated with the text-only subset.
### Usage Example
```python
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3.1")
messages = [
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "Who are you?"},
{"role": "assistant", "content": "<think>Hmm</think>I am DeepSeek"},
{"role": "user", "content": "1+1=?"}
]
tokenizer.apply_chat_template(messages, tokenize=False, thinking=True, add_generation_prompt=True)
# '<｜begin▁of▁sentence｜>You are a helpful assistant<｜User｜>Who are you?<｜Assistant｜></think>I am DeepSeek<｜end▁of▁sentence｜><｜User｜>1+1=?<｜Assistant｜><think>'
tokenizer.apply_chat_template(messages, tokenize=False, thinking=False, add_generation_prompt=True)
# '<｜begin▁of▁sentence｜>You are a helpful assistant<｜User｜>Who are you?<｜Assistant｜></think>I am DeepSeek<｜end▁of▁sentence｜><｜User｜>1+1=?<｜Assistant｜></think>'
```
## How to Run Locally
The model structure of DeepSeek-V3.1 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.
**Usage Recommendations:**
1. **The `mlp.gate.e_score_correction_bias `parameters should be loaded and computed in FP32 precision.**
2. **Ensure that FP8 model weights and activations are formatted using the UE8M0 scale format.**
## 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]).
|
Miracle12345/gemma-3-GRPO
|
Miracle12345
| 2025-09-05T11:23:07Z | 0 | 0 | null |
[
"safetensors",
"unsloth",
"license:apache-2.0",
"region:us"
] | null | 2025-09-05T11:20:53Z |
---
license: apache-2.0
tags:
- unsloth
---
|
AnerYubo/blockassist-bc-elusive_mammalian_termite_1757071371
|
AnerYubo
| 2025-09-05T11:22:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"elusive mammalian termite",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:22:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- elusive mammalian termite
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
enigmatic/Dreamscape_Urbanism_Qwen_LoRA
|
enigmatic
| 2025-09-05T11:21:26Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-05T11:16:51Z |
---
license: apache-2.0
---
|
zenqqq/blockassist-bc-restless_reptilian_caterpillar_1757071179
|
zenqqq
| 2025-09-05T11:21:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"restless reptilian caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:21:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- restless reptilian caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
silentone0725/merged_16bit
|
silentone0725
| 2025-09-05T11:20:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T10:39:05Z |
---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** silentone0725
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-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)
|
CoderBak/Qwen3-30B-A3B-Instruct-2507-EnergyQA-Expansion
|
CoderBak
| 2025-09-05T11:12:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"arxiv:2402.17463",
"arxiv:2407.02490",
"arxiv:2501.15383",
"arxiv:2404.06654",
"arxiv:2505.09388",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T06:53:30Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507/blob/main/LICENSE
pipeline_tag: text-generation
---
This a full parameter fine-tuning version of Qwen3-30B-A3B-Instruct-2507 which is trained on a large scale energy QA expansion dataset. This is the model at step 300.
# Qwen3-30B-A3B-Instruct-2507
<a href="https://chat.qwen.ai/?model=Qwen3-30B-A3B-2507" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Highlights
We introduce the updated version of the **Qwen3-30B-A3B non-thinking mode**, named **Qwen3-30B-A3B-Instruct-2507**, featuring the following key enhancements:
- **Significant improvements** in general capabilities, including **instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage**.
- **Substantial gains** in long-tail knowledge coverage across **multiple languages**.
- **Markedly better alignment** with user preferences in **subjective and open-ended tasks**, enabling more helpful responses and higher-quality text generation.
- **Enhanced capabilities** in **256K long-context understanding**.
## Model Overview
**Qwen3-30B-A3B-Instruct-2507** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 30.5B in total and 3.3B activated
- Number of Paramaters (Non-Embedding): 29.9B
- Number of Layers: 48
- Number of Attention Heads (GQA): 32 for Q and 4 for KV
- Number of Experts: 128
- Number of Activated Experts: 8
- Context Length: **262,144 natively**.
**NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Performance
| | Deepseek-V3-0324 | GPT-4o-0327 | Gemini-2.5-Flash Non-Thinking | Qwen3-235B-A22B Non-Thinking | Qwen3-30B-A3B Non-Thinking | Qwen3-30B-A3B-Instruct-2507 |
|--- | --- | --- | --- | --- | --- | --- |
| **Knowledge** | | | | | | |
| MMLU-Pro | **81.2** | 79.8 | 81.1 | 75.2 | 69.1 | 78.4 |
| MMLU-Redux | 90.4 | **91.3** | 90.6 | 89.2 | 84.1 | 89.3 |
| GPQA | 68.4 | 66.9 | **78.3** | 62.9 | 54.8 | 70.4 |
| SuperGPQA | **57.3** | 51.0 | 54.6 | 48.2 | 42.2 | 53.4 |
| **Reasoning** | | | | | | |
| AIME25 | 46.6 | 26.7 | **61.6** | 24.7 | 21.6 | 61.3 |
| HMMT25 | 27.5 | 7.9 | **45.8** | 10.0 | 12.0 | 43.0 |
| ZebraLogic | 83.4 | 52.6 | 57.9 | 37.7 | 33.2 | **90.0** |
| LiveBench 20241125 | 66.9 | 63.7 | **69.1** | 62.5 | 59.4 | 69.0 |
| **Coding** | | | | | | |
| LiveCodeBench v6 (25.02-25.05) | **45.2** | 35.8 | 40.1 | 32.9 | 29.0 | 43.2 |
| MultiPL-E | 82.2 | 82.7 | 77.7 | 79.3 | 74.6 | **83.8** |
| Aider-Polyglot | 55.1 | 45.3 | 44.0 | **59.6** | 24.4 | 35.6 |
| **Alignment** | | | | | | |
| IFEval | 82.3 | 83.9 | 84.3 | 83.2 | 83.7 | **84.7** |
| Arena-Hard v2* | 45.6 | 61.9 | 58.3 | 52.0 | 24.8 | **69.0** |
| Creative Writing v3 | 81.6 | 84.9 | 84.6 | 80.4 | 68.1 | **86.0** |
| WritingBench | 74.5 | 75.5 | 80.5 | 77.0 | 72.2 | **85.5** |
| **Agent** | | | | | | |
| BFCL-v3 | 64.7 | 66.5 | 66.1 | **68.0** | 58.6 | 65.1 |
| TAU1-Retail | 49.6 | 60.3# | **65.2** | 65.2 | 38.3 | 59.1 |
| TAU1-Airline | 32.0 | 42.8# | **48.0** | 32.0 | 18.0 | 40.0 |
| TAU2-Retail | **71.1** | 66.7# | 64.3 | 64.9 | 31.6 | 57.0 |
| TAU2-Airline | 36.0 | 42.0# | **42.5** | 36.0 | 18.0 | 38.0 |
| TAU2-Telecom | **34.0** | 29.8# | 16.9 | 24.6 | 18.4 | 12.3 |
| **Multilingualism** | | | | | | |
| MultiIF | 66.5 | 70.4 | 69.4 | 70.2 | **70.8** | 67.9 |
| MMLU-ProX | 75.8 | 76.2 | **78.3** | 73.2 | 65.1 | 72.0 |
| INCLUDE | 80.1 | 82.1 | **83.8** | 75.6 | 67.8 | 71.9 |
| PolyMATH | 32.2 | 25.5 | 41.9 | 27.0 | 23.3 | **43.1** |
*: For reproducibility, we report the win rates evaluated by GPT-4.1.
\#: Results were generated using GPT-4o-20241120, as access to the native function calling API of GPT-4o-0327 was unavailable.
## Quickstart
The code of Qwen3-MoE has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3_moe'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-30B-A3B-Instruct-2507"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=16384
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-30B-A3B-Instruct-2507 --context-length 262144
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 --max-model-len 262144
```
**Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.**
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-30B-A3B-Instruct-2507',
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Processing Ultra-Long Texts
To support **ultra-long context processing** (up to **1 million tokens**), we integrate two key techniques:
- **[Dual Chunk Attention](https://arxiv.org/abs/2402.17463) (DCA)**: A length extrapolation method that splits long sequences into manageable chunks while preserving global coherence.
- **[MInference](https://arxiv.org/abs/2407.02490)**: A sparse attention mechanism that reduces computational overhead by focusing on critical token interactions.
Together, these innovations significantly improve both **generation quality** and **inference efficiency** for sequences beyond 256K tokens. On sequences approaching 1M tokens, the system achieves up to a **3× speedup** compared to standard attention implementations.
For full technical details, see the [Qwen2.5-1M Technical Report](https://arxiv.org/abs/2501.15383).
### How to Enable 1M Token Context
> [!NOTE]
> To effectively process a 1 million token context, users will require approximately **240 GB** of total GPU memory. This accounts for model weights, KV-cache storage, and peak activation memory demands.
#### Step 1: Update Configuration File
Download the model and replace the content of your `config.json` with `config_1m.json`, which includes the config for length extrapolation and sparse attention.
```bash
export MODELNAME=Qwen3-30B-A3B-Instruct-2507
huggingface-cli download Qwen/${MODELNAME} --local-dir ${MODELNAME}
mv ${MODELNAME}/config.json ${MODELNAME}/config.json.bak
mv ${MODELNAME}/config_1m.json ${MODELNAME}/config.json
```
#### Step 2: Launch Model Server
After updating the config, proceed with either **vLLM** or **SGLang** for serving the model.
#### Option 1: Using vLLM
To run Qwen with 1M context support:
```bash
pip install -U vllm \
--torch-backend=auto \
--extra-index-url https://wheels.vllm.ai/nightly
```
Then launch the server with Dual Chunk Flash Attention enabled:
```bash
VLLM_ATTENTION_BACKEND=DUAL_CHUNK_FLASH_ATTN VLLM_USE_V1=0 \
vllm serve ./Qwen3-30B-A3B-Instruct-2507 \
--tensor-parallel-size 4 \
--max-model-len 1010000 \
--enable-chunked-prefill \
--max-num-batched-tokens 131072 \
--enforce-eager \
--max-num-seqs 1 \
--gpu-memory-utilization 0.85
```
##### Key Parameters
| Parameter | Purpose |
|--------|--------|
| `VLLM_ATTENTION_BACKEND=DUAL_CHUNK_FLASH_ATTN` | Enables the custom attention kernel for long-context efficiency |
| `--max-model-len 1010000` | Sets maximum context length to ~1M tokens |
| `--enable-chunked-prefill` | Allows chunked prefill for very long inputs (avoids OOM) |
| `--max-num-batched-tokens 131072` | Controls batch size during prefill; balances throughput and memory |
| `--enforce-eager` | Disables CUDA graph capture (required for dual chunk attention) |
| `--max-num-seqs 1` | Limits concurrent sequences due to extreme memory usage |
| `--gpu-memory-utilization 0.85` | Set the fraction of GPU memory to be used for the model executor |
#### Option 2: Using SGLang
First, clone and install the specialized branch:
```bash
git clone https://github.com/sgl-project/sglang.git
cd sglang
pip install -e "python[all]"
```
Launch the server with DCA support:
```bash
python3 -m sglang.launch_server \
--model-path ./Qwen3-30B-A3B-Instruct-2507 \
--context-length 1010000 \
--mem-frac 0.75 \
--attention-backend dual_chunk_flash_attn \
--tp 4 \
--chunked-prefill-size 131072
```
##### Key Parameters
| Parameter | Purpose |
|---------|--------|
| `--attention-backend dual_chunk_flash_attn` | Activates Dual Chunk Flash Attention |
| `--context-length 1010000` | Defines max input length |
| `--mem-frac 0.75` | The fraction of the memory used for static allocation (model weights and KV cache memory pool). Use a smaller value if you see out-of-memory errors. |
| `--tp 4` | Tensor parallelism size (matches model sharding) |
| `--chunked-prefill-size 131072` | Prefill chunk size for handling long inputs without OOM |
#### Troubleshooting:
1. Encountering the error: "The model's max sequence length (xxxxx) is larger than the maximum number of tokens that can be stored in the KV cache." or "RuntimeError: Not enough memory. Please try to increase --mem-fraction-static."
The VRAM reserved for the KV cache is insufficient.
- vLLM: Consider reducing the ``max_model_len`` or increasing the ``tensor_parallel_size`` and ``gpu_memory_utilization``. Alternatively, you can reduce ``max_num_batched_tokens``, although this may significantly slow down inference.
- SGLang: Consider reducing the ``context-length`` or increasing the ``tp`` and ``mem-frac``. Alternatively, you can reduce ``chunked-prefill-size``, although this may significantly slow down inference.
2. Encountering the error: "torch.OutOfMemoryError: CUDA out of memory."
The VRAM reserved for activation weights is insufficient. You can try lowering ``gpu_memory_utilization`` or ``mem-frac``, but be aware that this might reduce the VRAM available for the KV cache.
3. Encountering the error: "Input prompt (xxxxx tokens) + lookahead slots (0) is too long and exceeds the capacity of the block manager." or "The input (xxx xtokens) is longer than the model's context length (xxx tokens)."
The input is too lengthy. Consider using a shorter sequence or increasing the ``max_model_len`` or ``context-length``.
#### Long-Context Performance
We test the model on an 1M version of the [RULER](https://arxiv.org/abs/2404.06654) benchmark.
| Model Name | Acc avg | 4k | 8k | 16k | 32k | 64k | 96k | 128k | 192k | 256k | 384k | 512k | 640k | 768k | 896k | 1000k |
|---------------------------------------------|---------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|-------|
| Qwen3-30B-A3B (Non-Thinking) | 72.0 | 97.1 | 96.1 | 95.0 | 92.2 | 82.6 | 79.7 | 76.9 | 70.2 | 66.3 | 61.9 | 55.4 | 52.6 | 51.5 | 52.0 | 50.9 |
| Qwen3-30B-A3B-Instruct-2507 (Full Attention) | 86.8 | 98.0 | 96.7 | 96.9 | 97.2 | 93.4 | 91.0 | 89.1 | 89.8 | 82.5 | 83.6 | 78.4 | 79.7 | 77.6 | 75.7 | 72.8 |
| Qwen3-30B-A3B-Instruct-2507 (Sparse Attention) | 86.8 | 98.0 | 97.1 | 96.3 | 95.1 | 93.6 | 92.5 | 88.1 | 87.7 | 82.9 | 85.7 | 80.7 | 80.0 | 76.9 | 75.5 | 72.2 |
* All models are evaluated with Dual Chunk Attention enabled.
* Since the evaluation is time-consuming, we use 260 samples for each length (13 sub-tasks, 20 samples for each).
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- We suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 16,384 tokens for most queries, which is adequate for instruct models.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3technicalreport,
title={Qwen3 Technical Report},
author={Qwen Team},
year={2025},
eprint={2505.09388},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.09388},
}
```
|
Miracle-man/blockassist-bc-singing_lithe_koala_1757068664
|
Miracle-man
| 2025-09-05T11:11:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"singing lithe koala",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:11:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- singing lithe koala
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
NahedDom/blockassist-bc-flapping_stocky_leopard_1757068383
|
NahedDom
| 2025-09-05T11:09:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:09:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping stocky leopard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayan01/Qwen-1.5-0.5B-DFD-10-0
|
Sayan01
| 2025-09-05T11:07:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T11:05:56Z |
---
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]
|
aleebaster/blockassist-bc-sly_eager_boar_1757068692
|
aleebaster
| 2025-09-05T11:04:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:04:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly eager boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kafa22/blockassist-bc-regal_leggy_hummingbird_1757070224
|
kafa22
| 2025-09-05T11:04:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal leggy hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:04:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal leggy hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jackven248/blockassist-bc-poisonous_barky_alpaca_1757070176
|
jackven248
| 2025-09-05T11:03:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"poisonous barky alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:03:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- poisonous barky alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/the-age-of-innocence-j.c.-leyendecker-illustration-style
|
Muapi
| 2025-09-05T11:03:25Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-05T11:03:08Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# The Age of Innocence: J.C. Leyendecker Illustration Style
**Base model**: Flux.1 D
**Trained words**: jcleyen1 painting
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1194777@1345229", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
mtaimoorhassan/qalb-llm-8b
|
mtaimoorhassan
| 2025-09-05T10:56:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"urdu",
"pakistan",
"fine-tuned",
"bilingual",
"ur",
"en",
"dataset:custom-urdu-corpus",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:quantized:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-09-05T10:55:13Z |
---
language:
- ur
- en
license: llama3.1
tags:
- llama
- urdu
- pakistan
- text-generation
- fine-tuned
- bilingual
base_model: meta-llama/Meta-Llama-3.1-8B
datasets:
- custom-urdu-corpus
metrics:
- perplexity
library_name: transformers
pipeline_tag: text-generation
---
# Llama 3.1 8B - Urdu Fine-tuned (Improved)
This model is an improved version of Llama 3.1 8B specifically fine-tuned for Urdu language generation while preserving the original English and general knowledge capabilities.
## 🌟 Key Features
- ✅ **Bilingual**: Excellent performance in both Urdu and English
- ✅ **Knowledge Preservation**: Retains original Llama 3.1 knowledge and reasoning
- ✅ **Urdu Expertise**: High-quality Urdu text generation for essays, articles, and content
- ✅ **Conservative Merge**: Uses advanced merging techniques to preserve base capabilities
## 📊 Model Details
- **Base Model**: Meta-Llama-3.1-8B
- **Languages**: Urdu (اردو) + English (preserved)
- **Training Method**: LoRA fine-tuning with conservative merge
- **Training Steps**: 50,000
- **LoRA Rank**: 64
- **Parameters**: ~8.5B (additional 40,960 from fine-tuning)
- **Vocabulary**: 128,261 tokens (base + Urdu special tokens)
## 🚀 Usage
### Quick Start
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name = "mtaimoorhassan/qalb-llm-8b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# English generation
prompt = "Explain the importance of education:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# Urdu generation
prompt = "اردو میں مضمون لکھیں: تعلیم کی اہمیت"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.8)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
### Advanced Usage
```python
class UrduLlamaGenerator:
def __init__(self, model_name="mtaimoorhassan/qalb-llm-8b"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
def generate(self, prompt, max_length=300, temperature=0.7):
# Language-aware generation
is_urdu = any(char in 'ابپتٹثجچحخدڈذرڑزژسشصضطظعغفقکگلمنںوہھیے' for char in prompt)
inputs = self.tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_new_tokens=max_length,
temperature=temperature + (0.1 if is_urdu else 0),
top_p=0.95 if is_urdu else 0.9,
repetition_penalty=1.05,
do_sample=True,
)
return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
# Usage
generator = UrduLlamaGenerator()
response = generator.generate("اردو میں بتائیں: علامہ اقبال کون تھے؟")
print(response)
```
## 📚 Training Details
### Dataset
- **Source**: Large-scale Urdu corpus (50,000+ samples)
- **Content**: Essays, articles, educational content, literature
- **Preprocessing**: Advanced cleaning and formatting for optimal training
### Training Configuration
- **Method**: LoRA (Low-Rank Adaptation)
- **Rank**: 64 (high-rank for maximum adaptation)
- **Alpha**: 128 (2x scaling for enhanced learning)
- **Target Modules**: All attention and MLP layers + embeddings
- **Learning Rate**: 1e-5 (conservative)
- **Batch Size**: 8 (effective)
- **Training Steps**: 50,000
- **Hardware**: NVIDIA A100 80GB
### Merge Strategy
- **Type**: Conservative merge preserving base knowledge
- **Special Tokens**: Minimal addition (5 tokens)
- **Knowledge Preservation**: ✅ Maintains English capabilities
- **Urdu Enhancement**: ✅ Adds high-quality Urdu generation
## 🎯 Performance
### Test Results (Average: 4.5/5 ⭐)
| Category | Score | Description |
|----------|-------|-------------|
| English Knowledge | 5/5 ⭐ | Excellent factual accuracy |
| General Reasoning | 4/5 ⭐ | Strong logical capabilities |
| Urdu Generation | 4/5 ⭐ | High-quality Urdu text |
| Bilingual Handling | 5/5 ⭐ | Seamless language switching |
### Sample Outputs
**English Knowledge:**
```
Q: What is the capital of France?
A: Paris, the capital and largest city of France, located in northern France...
```
**Urdu Biography:**
```
Q: اردو میں علامہ اقبال کون تھے؟
A: علامہ محمد اقبال (1877-1938) ایک عظیم شاعر، فلسفی، اور سیاست دان تھے۔ وہ پاکستان کے روحانی باپ تسلیم کیے جاتے ہیں...
```
## ⚠️ Limitations
- Some minor character encoding issues in complex Urdu text
- Occasional repetition in very long generations
- Best performance with clear, well-formed prompts
- Requires GPU for optimal inference speed
## 📄 License
This model follows the Llama 3.1 license. Please ensure compliance with Meta's usage terms.
## 🙏 Acknowledgments
- Built on Meta's Llama 3.1 8B foundation model
- Fine-tuned using Unsloth for efficient training
- Developed for enhancing Urdu language AI capabilities
## 📞 Contact
For questions, improvements, or collaborations, please open an issue on the repository.
---
*This model represents a significant step forward in Urdu language AI, combining the power of Llama 3.1 with specialized Urdu knowledge while maintaining multilingual capabilities.*
|
Signvrse/Glosser_Gemma2_2B
|
Signvrse
| 2025-09-05T10:55:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma2",
"trl",
"en",
"base_model:unsloth/gemma-2-2b-bnb-4bit",
"base_model:finetune:unsloth/gemma-2-2b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-26T09:21:29Z |
---
base_model: unsloth/gemma-2-2b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Signvrse
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2-2b-bnb-4bit
This gemma2 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)
|
jackven248/blockassist-bc-poisonous_barky_alpaca_1757069706
|
jackven248
| 2025-09-05T10:55:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"poisonous barky alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:55:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- poisonous barky alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1757069615
|
arif696
| 2025-09-05T10:54:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:54:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
atulchief/blockassist-bc-nimble_mighty_cat_1757069483
|
atulchief
| 2025-09-05T10:53:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"nimble mighty cat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:52:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- nimble mighty cat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jackven248/blockassist-bc-poisonous_barky_alpaca_1757068984
|
jackven248
| 2025-09-05T10:44:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"poisonous barky alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:44:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- poisonous barky alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1757068966
|
arif696
| 2025-09-05T10:44:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:43:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sekirr/blockassist-bc-masked_tenacious_whale_1757068872
|
sekirr
| 2025-09-05T10:41:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:41:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
zxcczx/blockassist-bc-durable_energetic_fly_1757067840
|
zxcczx
| 2025-09-05T10:40:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"durable energetic fly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:40:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- durable energetic fly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Tobias-B/wav2vec2-large-xlsr-ipa-augmentation-plosive_phonation-baseline
|
Tobias-B
| 2025-09-05T10:40:11Z | 3 | 0 | null |
[
"pytorch",
"wav2vec2",
"speech, phonetics, ipa",
"dataset:common_voice_11_0",
"license:apache-2.0",
"region:us"
] | null | 2025-04-25T10:06:04Z |
---
datasets:
- common_voice_11_0
tags:
- speech, phonetics, ipa
license: apache-2.0
---
**Use THIS model**: https://huggingface.co/Tobias-B/wav2vec2-large-xlsr-ipa-augmentation-plosive_phonation-target
# Baseline Model (BM) for Selective Augmentation:
https://huggingface.co/collections/Tobias-B/universal-phonetic-asr-models-selective-augmentation-680b5034c0729058fadcf1d6
These models were created to advance automatic phonetic transcription (APT) beyond the training transcription accuracy.
The workflow to improve APT is called selective augmentation and was developed in Tobias Bystrich’s master’s thesis "Multilingual Automatic Phonetic Transcription – a Linguistic Investigation of its Performance on German and Approaches to Improving the State of the Art".
https://doi.org/10.24406/publica-4418
This thesis was written at Fraunhofer Institute IAIS and with the resources of WestAI: Simulations were performed with computing resources granted by WestAI under project rwth1594.
The models in this repository are the reference (RM), helper (HM), baseline (BM) and target model (TM) for the selective augmentation workflow. Additionally, for reimplementation, the provided list of training segments ensures that the RM can predict the highest quality reference transcriptions.
The RM closely corresponds to a reimplemented MultIPA model (https://github.com/ctaguchi/multipa).
The target model has greatly improved plosive phonation information when measured against the baseline model. This is achieved by augmenting the baseline training data with reliable phonation information from a Hindi helper model.
|
Tobias-B/wav2vec2-large-xlsr-ipa-augmentation-plosive_phonation-helper
|
Tobias-B
| 2025-09-05T10:39:36Z | 3 | 0 | null |
[
"pytorch",
"wav2vec2",
"speech, phonetics, ipa",
"hi",
"dataset:common_voice_11_0",
"license:apache-2.0",
"region:us"
] | null | 2025-04-25T10:04:35Z |
---
language: hi
datasets:
- common_voice_11_0
tags:
- speech, phonetics, ipa
license: apache-2.0
---
**Use THIS model**:
https://huggingface.co/Tobias-B/wav2vec2-large-xlsr-ipa-augmentation-plosive_phonation-target
# (Plosive Phonation) Helper Model (HM) for Selective Augmentation:
https://huggingface.co/collections/Tobias-B/universal-phonetic-asr-models-selective-augmentation-680b5034c0729058fadcf1d6
These models were created to advance automatic phonetic transcription (APT) beyond the training transcription accuracy.
The workflow to improve APT is called selective augmentation and was developed in Tobias Bystrich’s master’s thesis "Multilingual Automatic Phonetic Transcription – a Linguistic Investigation of its Performance on German and Approaches to Improving the State of the Art".
https://doi.org/10.24406/publica-4418
This thesis was written at Fraunhofer Institute IAIS and with the resources of WestAI: Simulations were performed with computing resources granted by WestAI under project rwth1594.
The models in this project are the reference (RM), helper (HM), baseline (BM) and target model (TM) for the selective augmentation workflow. Additionally, for reimplementation, the provided list of training segments ensures that the RM can predict the highest quality reference transcriptions.
The RM closely corresponds to a reimplemented MultIPA model (https://github.com/ctaguchi/multipa).
The target model has greatly improved plosive phonation information when measured against the baseline model. This is achieved by augmenting the baseline training data with reliable phonation information from a Hindi helper model.
|
despoinakk/diffusion_cosine_babylm
|
despoinakk
| 2025-09-05T10:38:38Z | 5,906 | 0 | null |
[
"custom_code",
"license:apache-2.0",
"region:us"
] | null | 2025-08-18T06:33:36Z |
---
license: apache-2.0
---
|
mradermacher/SmolLM2-Rethink-135M-GGUF
|
mradermacher
| 2025-09-05T10:38:02Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"trl",
"text-generation-inference",
"re-think",
"reasoning",
"en",
"dataset:sequelbox/Celestia3-DeepSeek-R1-0528",
"base_model:prithivMLmods/SmolLM2-Rethink-135M",
"base_model:quantized:prithivMLmods/SmolLM2-Rethink-135M",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-05T10:34:57Z |
---
base_model: prithivMLmods/SmolLM2-Rethink-135M
datasets:
- sequelbox/Celestia3-DeepSeek-R1-0528
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- trl
- text-generation-inference
- re-think
- reasoning
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/prithivMLmods/SmolLM2-Rethink-135M
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#SmolLM2-Rethink-135M-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-Rethink-135M-GGUF/resolve/main/SmolLM2-Rethink-135M.Q2_K.gguf) | Q2_K | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-Rethink-135M-GGUF/resolve/main/SmolLM2-Rethink-135M.Q3_K_S.gguf) | Q3_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-Rethink-135M-GGUF/resolve/main/SmolLM2-Rethink-135M.IQ4_XS.gguf) | IQ4_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-Rethink-135M-GGUF/resolve/main/SmolLM2-Rethink-135M.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-Rethink-135M-GGUF/resolve/main/SmolLM2-Rethink-135M.Q3_K_L.gguf) | Q3_K_L | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-Rethink-135M-GGUF/resolve/main/SmolLM2-Rethink-135M.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-Rethink-135M-GGUF/resolve/main/SmolLM2-Rethink-135M.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-Rethink-135M-GGUF/resolve/main/SmolLM2-Rethink-135M.Q5_K_S.gguf) | Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-Rethink-135M-GGUF/resolve/main/SmolLM2-Rethink-135M.Q5_K_M.gguf) | Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-Rethink-135M-GGUF/resolve/main/SmolLM2-Rethink-135M.Q6_K.gguf) | Q6_K | 0.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-Rethink-135M-GGUF/resolve/main/SmolLM2-Rethink-135M.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-Rethink-135M-GGUF/resolve/main/SmolLM2-Rethink-135M.f16.gguf) | f16 | 0.4 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
arif696/blockassist-bc-regal_spotted_pelican_1757068505
|
arif696
| 2025-09-05T10:36:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:36:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1757067095
|
vwzyrraz7l
| 2025-09-05T10:35:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:35:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kiok1250/blockassist-bc-beaked_insectivorous_lobster_1757068030
|
kiok1250
| 2025-09-05T10:28:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked insectivorous lobster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:27:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked insectivorous lobster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Bolton12/blockassist-bc-rangy_yawning_impala_1757066075
|
Bolton12
| 2025-09-05T10:24:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rangy yawning impala",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:24:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rangy yawning impala
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/marin-8b-instruct-GGUF
|
mradermacher
| 2025-09-05T10:23:54Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"en",
"dataset:TIGER-Lab/AceCode-87K",
"dataset:bespokelabs/Bespoke-Stratos-17k",
"dataset:cognitivecomputations/dolphin-r1",
"dataset:tuenguyen/dolphin_r1_reasoning",
"dataset:facebook/natural_reasoning",
"dataset:open-r1/OpenThoughts-114k-math",
"dataset:HuggingFaceTB/smoltalk",
"base_model:marin-community/marin-8b-instruct",
"base_model:quantized:marin-community/marin-8b-instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-09-05T09:45:33Z |
---
base_model: marin-community/marin-8b-instruct
datasets:
- TIGER-Lab/AceCode-87K
- bespokelabs/Bespoke-Stratos-17k
- cognitivecomputations/dolphin-r1
- tuenguyen/dolphin_r1_reasoning
- facebook/natural_reasoning
- open-r1/OpenThoughts-114k-math
- HuggingFaceTB/smoltalk
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/marin-community/marin-8b-instruct
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#marin-8b-instruct-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/marin-8b-instruct-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/marin-8b-instruct-GGUF/resolve/main/marin-8b-instruct.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/marin-8b-instruct-GGUF/resolve/main/marin-8b-instruct.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/marin-8b-instruct-GGUF/resolve/main/marin-8b-instruct.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/marin-8b-instruct-GGUF/resolve/main/marin-8b-instruct.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/marin-8b-instruct-GGUF/resolve/main/marin-8b-instruct.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/marin-8b-instruct-GGUF/resolve/main/marin-8b-instruct.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/marin-8b-instruct-GGUF/resolve/main/marin-8b-instruct.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/marin-8b-instruct-GGUF/resolve/main/marin-8b-instruct.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/marin-8b-instruct-GGUF/resolve/main/marin-8b-instruct.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/marin-8b-instruct-GGUF/resolve/main/marin-8b-instruct.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/marin-8b-instruct-GGUF/resolve/main/marin-8b-instruct.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/marin-8b-instruct-GGUF/resolve/main/marin-8b-instruct.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
moyixiao/Qwen3-0.6B-dr-f16-100
|
moyixiao
| 2025-09-05T10:19:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T10:19:27Z |
---
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]
|
upvantage/modernbert-KK-group1
|
upvantage
| 2025-09-05T10:19:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"base_model:answerdotai/ModernBERT-large",
"base_model:finetune:answerdotai/ModernBERT-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-05T09:46:23Z |
---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: modernbert-KK-group1
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. -->
# modernbert-KK-group1
This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1486
- Accuracy: 0.9405
- F1: 0.9405
- Precision: 0.9406
- Recall: 0.9405
- F1 Class 0: 0.9423
- Precision Class 0: 0.9367
- Recall Class 0: 0.9479
- F1 Class 1: 0.9386
- Precision Class 1: 0.9446
- Recall Class 1: 0.9327
## 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: 1e-05
- train_batch_size: 600
- eval_batch_size: 600
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 4800
- total_eval_batch_size: 4800
- 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_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | F1 Class 0 | Precision Class 0 | Recall Class 0 | F1 Class 1 | Precision Class 1 | Recall Class 1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:-----------------:|:--------------:|
| 1.1704 | 1.0 | 18050 | 0.1486 | 0.9405 | 0.9405 | 0.9406 | 0.9405 | 0.9423 | 0.9367 | 0.9479 | 0.9386 | 0.9446 | 0.9327 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
|
arif696/blockassist-bc-regal_spotted_pelican_1757067465
|
arif696
| 2025-09-05T10:19:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:19:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1757067503
|
bah63843
| 2025-09-05T10:19:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:19:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ayda138000/controlnet_persian_text_v1
|
ayda138000
| 2025-09-05T10:19:08Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"diffusers-training",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-09-05T09:40:52Z |
---
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# controlnet-ayda138000/controlnet_persian_text_v1
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
You can find some example images below.
prompt: یک لوگوی مدرن برای یک شرکت فناوری پیشرفته
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
efrat-dev/phi3-mini-jewish-suffix-adapter
|
efrat-dev
| 2025-09-05T10:19:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:finetune:microsoft/Phi-3-mini-4k-instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-02T21:11:07Z |
---
base_model: microsoft/Phi-3-mini-4k-instruct
library_name: transformers
model_name: phi3-mini-jewish-suffix-adapter
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for phi3-mini-jewish-suffix-adapter
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-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="efrat-dev/phi3-mini-jewish-suffix-adapter", 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: 0.12.1
- Transformers: 4.46.2
- Pytorch: 2.8.0+cu126
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
sekirr/blockassist-bc-masked_tenacious_whale_1757067458
|
sekirr
| 2025-09-05T10:18:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:18:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kalimoy/blockassist-bc-smooth_aquatic_turtle_1757067147
|
kalimoy
| 2025-09-05T10:12:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth aquatic turtle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:12:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth aquatic turtle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ashishscapsitech123/qwen25_7b_4bit_3400_full_finetuned
|
ashishscapsitech123
| 2025-09-05T10:12:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"unsloth",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
image-to-text
| 2025-09-05T10:09:50Z |
---
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]
### 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]
|
casvxzv/blockassist-bc-carnivorous_quick_beaver_1757066955
|
casvxzv
| 2025-09-05T10:09:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"carnivorous quick beaver",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:09:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- carnivorous quick beaver
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
raihannabiil/blockassist-bc-humming_rugged_viper_1757064662
|
raihannabiil
| 2025-09-05T10:08:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"humming rugged viper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:08:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- humming rugged viper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cactus-S/blockassist-bc-reclusive_arctic_panther_1757065418
|
cactus-S
| 2025-09-05T10:08:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive arctic panther",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:08:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive arctic panther
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
SnJake/JPG_Noise_Remover
|
SnJake
| 2025-09-05T10:07:55Z | 0 | 0 | null |
[
"computer-vision",
"image-restoration",
"jpeg-artifacts",
"denoising",
"comfyui",
"license:mit",
"region:us"
] | null | 2025-09-05T03:59:44Z |
---
license: mit
tags:
- computer-vision
- image-restoration
- jpeg-artifacts
- denoising
- comfyui
---
# About this project
This project is a personal experiment created out of curiosity. The main part of the code was generated by an AI assistant, and my task was to set the goal, prepare the data, run the training and evaluate the result. The model is trained to remove artifacts from images (JPEG, noise) and even shows good results.
## Artifacts Remover UNet
This is a lightweight UNet-based model trained to remove JPEG compression artifacts and additive Gaussian noise from images. The model is ideal for integration into image processing pipelines, including the popular ComfyUI framework.
## Examples
### How to use in ComfyUI
This is the primary way to use this model.
1. **Install the custom node**:
```bash
cd ComfyUI/custom_nodes
git clone https://github.com/SnJake/SnJakeArtifactsRemover.git
```
2. **Download the weights**: Download the `best_ema_15E.pt` file (or another `.safetensors` file) from the "Files and versions" tab of this repository.
3. **Place the weights**: Create a folder named `artifacts_remover` inside `ComfyUI/models/` and place the downloaded file there.
* The final path should be: `ComfyUI/models/artifacts_remover/best_ema.pt`
4. **Run ComfyUI**: The `😎 JPG & Noise Remover` node will be available in the "Add Node" menu. It will automatically detect the downloaded weights.
### Training Details
The model was trained on a dataset of approximately 30,000 high-quality images, primarily consisting of anime-style art. Instead of using pre-degraded images, the training process generated (degraded, clean) image pairs on-the-fly.
* **Architecture**: The network is a `UNetRestorer` built with `ResidualBlock`s for deep feature extraction. To enhance important features, the deeper levels of the encoder utilize the Convolutional Block Attention Module (CBAM). The model employs a final residual connection, learning to predict the difference (`clean - degraded`) rather than the entire clean image.
* **Degradation Process**: Each clean image patch was subjected to a sequence of randomly ordered degradations:
* **JPEG Compression**: A random quality level was chosen between 5 and 85.
* **Gaussian Noise**: Gaussian noise was added with a standard deviation randomly selected from the range [0.0, 7.0].
* **Identity Mapping**: With a 20% probability (`--clean-prob 0.2`), the input image was left clean (not degraded). This encourages the model to preserve details when no artifacts are present.
* **Training Procedure**:
* **Optimizer**: AdamW with a learning rate of `2e-4` and weight decay of `1e-4`.
* **Learning Rate Scheduler**: A Cosine Annealing scheduler with a linear warmup phase of 2000 steps was used.
* **Batch & Patch Size**: The model was trained with a batch size of 12 using 320x320 pixel patches.
* **Loss Function**: A comprehensive, multi-component loss function was employed to balance pixel accuracy, structural integrity, and perceptual quality:
* **Primary Loss**: A weighted sum of `0.7 * CharbonnierLoss` (a smooth L1 variant) and `0.3 * MixL1SSIM`. The `MixL1SSIM` component itself was weighted with `alpha=0.9`, combining L1 loss and a structural similarity term (`0.9*L1 + 0.1*(1-SSIM)`).
* **Edge Loss**: `GradientLoss` was added with a weight of 0.15 (`--edge-loss-w 0.15`) to penalize blurry edges and promote sharpness.
* **High-Frequency Error Norm (HFEN)**: To better preserve fine textures and details, `HFENLoss` was included with a weight of 0.12 (`--hfen-w 0.12`).
* **Identity Loss**: For the 20% of samples where the input was clean, an additional L1 loss with a weight of 0.5 (`--id-loss-w 0.5`) was calculated between the model's output and the input. This forces the network to act as an identity function for high-quality images, preventing it from introducing blur or altering details.
* **Techniques**: Training was accelerated using Automatic Mixed Precision (AMP) with the `bfloat16` data type. An Exponential Moving Average (EMA) of the model's weights (`decay=0.999`) was maintained to produce a more stable and generalized final model for inference.
### Limitations and Potential Issues
* The model was trained on primarily consisting of anime-style art. Results on photos, line art, or text may be suboptimal.
* With very high levels of noise or artifacts beyond the training range, the model may hallucinate details or over-smooth the image.
* The model might interpret very fine, low-contrast textures (e.g., fabric, sand) as noise and smooth them out. For such cases, use the `blend` parameter in the node to mix back some of the original detail.
* The model does not correct for other types of degradation, such as motion blur, chromatic aberrations, or optical flaws.
|
arif696/blockassist-bc-regal_spotted_pelican_1757066682
|
arif696
| 2025-09-05T10:05:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:05:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
boomeryop/blockassist-bc-stinky_diving_viper_1757066698
|
boomeryop
| 2025-09-05T10:05:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinky diving viper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:04:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinky diving viper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
y1y2y3/so101_test4_diffusion_12k
|
y1y2y3
| 2025-09-05T10:02:42Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"diffusion",
"robotics",
"dataset:y1y2y3/so101_test4",
"arxiv:2303.04137",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-04T07:46:16Z |
---
datasets: y1y2y3/so101_test4
library_name: lerobot
license: apache-2.0
model_name: diffusion
pipeline_tag: robotics
tags:
- lerobot
- diffusion
- 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
|
vendi11/blockassist-bc-placid_placid_llama_1757066388
|
vendi11
| 2025-09-05T10:00:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T10:00:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
giovannidemuri/llama8b-er-v587-seed2-hx_lora
|
giovannidemuri
| 2025-09-05T09:58:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T08:00:35Z |
---
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]
|
arif696/blockassist-bc-regal_spotted_pelican_1757066050
|
arif696
| 2025-09-05T09:55:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T09:55:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1757066040
|
bah63843
| 2025-09-05T09:54:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T09:54:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
samunder12/llama-3.1-8b-Rp-tadashinu-gguf
|
samunder12
| 2025-09-05T09:54:02Z | 525 | 4 |
transformers
|
[
"transformers",
"gguf",
"llama",
"roleplay",
"rp",
"character",
"peft",
"unsloth",
"llama-3.1",
"instruct",
"creative-writing",
"storytelling",
"text-generation",
"en",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-8B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-09-04T07:46:58Z |
---
library_name: transformers
language: en
license: apache-2.0
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text-generation
tags:
- roleplay
- rp
- character
- peft
- unsloth
- llama-3.1
- instruct
- creative-writing
- storytelling
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="./tadashinu.jpg" alt="Peach" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
# llama-3.1-8b-Rp-tadashinu-gguf - A dark , immersive , dialogue ready , High-Concept Storyteller and Roleplayer
## Model Details
- **Base Model:** `unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit`
- **Original LoRA Model:** [`samunder12/llama-3.1-8b-roleplay-v5-lora`](https://huggingface.co/samunder12/llama-3.1-8b-roleplay-v5-lora)
- **Fine-tuning Method:** PEFT (LoRA) with Unsloth's performance optimizations.
- **LoRA Rank (`r`):** 64
- **Format:** GGUF
- **Quantization:** Q4_K_M
- **context_window** 4096
**llama-3.1-8b-Rp-tadashinu-gguf** is a fine-tuned version of Llama 3.1 8B Instruct, specifically crafted to be a master of high-concept, witty immersive , and darkly , intense creative writing.
This isn't your average storyteller. Trained on a curated dataset of absurd and imaginative scenarios—from sentient taxidermy raccoons to cryptid dating apps—this model excels at generating unique characters, crafting engaging scenes, and building fantastical worlds with a distinct, cynical voice. If you need a creative partner to brainstorm the bizarre, this is the model for you.
This model was fine-tuned using the Unsloth library for peak performance and memory efficiency.
**Provided files:**
* LoRA adapter for use with the base model.
* **GGUF (`q4_k_m`)** version for easy inference on local machines with `llama.cpp`, LM Studio, Ollama, etc.
## 💡 Intended Use & Use Cases
This model is designed for creative and entertainment purposes. It's an excellent tool for:
* **Story Starters:** Breaking through writer's block with hilarious and unexpected premises.
* **Character Creation:** Generating unique character bios with strong, memorable voices.
* **Scene Generation:** Writing short, punchy scenes in a dark comedy or absurd fantasy style.
* **Roleplaying:** Powering a game master or character with a witty, unpredictable personality.
* **Creative Brainstorming:** Generating high-concept ideas for stories, games, or scripts.
## 🔧 How to Use
### With Transformers (and Unsloth)
This model is a LoRA adapter. You must load it on top of the base model, `unsloth/meta-llama-3.1-8b-instruct-bnb-4bit`.
```python
from unsloth import FastLanguageModel
from transformers import TextStreamer
model_repo = "samunder12/llama-3.1-8b-roleplay-v5-lora"
base_model_repo = "unsloth/meta-llama-3.1-8b-instruct-bnb-4bit"
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = model_repo,
base_model = base_model_repo,
max_seq_length = 4096,
dtype = None,
load_in_4bit = True,
)
# --- Your system prompt ----
system_prompt = "You are a creative and witty storyteller." # A simple prompt is best
user_message = "A timid barista discovers their latte art predicts the future. Describe a chaotic morning when their foam sketches start depicting ridiculous alien invasions."
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_message},
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
text_streamer = TextStreamer(tokenizer)
_ = model.generate(inputs, streamer=text_streamer, max_new_tokens=512)
```
With GGUF
The provided GGUF file (q4_k_m quantization) can be used with any llama.cpp compatible client, such as:
LM Studio: Search for your model name **samunder12/llama-3.1-8b-Rp-tadashinu-gguf** directly in the app.
Ollama: Create a Modelfile pointing to the local GGUF file.
text-generation-webui: Place the GGUF file in your models directory and load it.
Remember to use the correct Llama 3.1 Instruct prompt template.
📝 Prompting Format
This model follows the official Llama 3.1 Instruct chat template. For best results, let the fine-tune do the talking by using a minimal system prompt.
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{your_system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{your_user_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
|
kelly45/gpt-oss-20b-ss-v4
|
kelly45
| 2025-09-05T09:53:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"endpoints_compatible",
"region:us"
] | null | 2025-09-05T09:37:45Z |
---
base_model: openai/gpt-oss-20b
library_name: transformers
model_name: gpt-oss-20b-ss-v4
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gpt-oss-20b-ss-v4
This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b).
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="kelly45/gpt-oss-20b-ss-v4", 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: 0.22.2
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu128
- 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}}
}
```
|
mradermacher/Sexpedition-MS3.2-24B-i1-GGUF
|
mradermacher
| 2025-09-05T09:52:17Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:Aleteian/Sexpedition-MS3.2-24B",
"base_model:quantized:Aleteian/Sexpedition-MS3.2-24B",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-09-05T08:09:25Z |
---
base_model: Aleteian/Sexpedition-MS3.2-24B
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/Aleteian/Sexpedition-MS3.2-24B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Sexpedition-MS3.2-24B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-IQ1_S.gguf) | i1-IQ1_S | 5.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-IQ1_M.gguf) | i1-IQ1_M | 5.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-IQ2_S.gguf) | i1-IQ2_S | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-IQ2_M.gguf) | i1-IQ2_M | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 8.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-Q2_K.gguf) | i1-Q2_K | 9.0 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 9.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 10.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-IQ3_S.gguf) | i1-IQ3_S | 10.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-IQ3_M.gguf) | i1-IQ3_M | 10.8 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 11.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 12.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 12.9 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-Q4_0.gguf) | i1-Q4_0 | 13.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 13.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-Q4_1.gguf) | i1-Q4_1 | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 16.4 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 16.9 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF/resolve/main/Sexpedition-MS3.2-24B.i1-Q6_K.gguf) | i1-Q6_K | 19.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Miracle-man/blockassist-bc-singing_lithe_koala_1757063981
|
Miracle-man
| 2025-09-05T09:50:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"singing lithe koala",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T09:50:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- singing lithe koala
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Grinding/fine_tuned_qwen_investment_bot_adapters
|
Grinding
| 2025-09-05T09:48:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-05T09:48:37Z |
---
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]
- **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]
|
gensynme/blockassist-bc-grunting_squinting_clam_1757065566
|
gensynme
| 2025-09-05T09:46:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"grunting squinting clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T09:46:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- grunting squinting clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Sexpedition-MS3.2-24B-GGUF
|
mradermacher
| 2025-09-05T09:45:55Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:Aleteian/Sexpedition-MS3.2-24B",
"base_model:quantized:Aleteian/Sexpedition-MS3.2-24B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-05T06:10:41Z |
---
base_model: Aleteian/Sexpedition-MS3.2-24B
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Aleteian/Sexpedition-MS3.2-24B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Sexpedition-MS3.2-24B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-GGUF/resolve/main/Sexpedition-MS3.2-24B.Q2_K.gguf) | Q2_K | 9.0 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-GGUF/resolve/main/Sexpedition-MS3.2-24B.Q3_K_S.gguf) | Q3_K_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-GGUF/resolve/main/Sexpedition-MS3.2-24B.Q3_K_M.gguf) | Q3_K_M | 11.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-GGUF/resolve/main/Sexpedition-MS3.2-24B.Q3_K_L.gguf) | Q3_K_L | 12.5 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-GGUF/resolve/main/Sexpedition-MS3.2-24B.IQ4_XS.gguf) | IQ4_XS | 13.0 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-GGUF/resolve/main/Sexpedition-MS3.2-24B.Q4_K_S.gguf) | Q4_K_S | 13.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-GGUF/resolve/main/Sexpedition-MS3.2-24B.Q4_K_M.gguf) | Q4_K_M | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-GGUF/resolve/main/Sexpedition-MS3.2-24B.Q5_K_S.gguf) | Q5_K_S | 16.4 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-GGUF/resolve/main/Sexpedition-MS3.2-24B.Q5_K_M.gguf) | Q5_K_M | 16.9 | |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-GGUF/resolve/main/Sexpedition-MS3.2-24B.Q6_K.gguf) | Q6_K | 19.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Sexpedition-MS3.2-24B-GGUF/resolve/main/Sexpedition-MS3.2-24B.Q8_0.gguf) | Q8_0 | 25.2 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Falcon3-7B-Instruct_openbookqa-GGUF
|
mradermacher
| 2025-09-05T09:39:00Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"en",
"base_model:jahyungu/Falcon3-7B-Instruct_openbookqa",
"base_model:quantized:jahyungu/Falcon3-7B-Instruct_openbookqa",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-05T08:35:52Z |
---
base_model: jahyungu/Falcon3-7B-Instruct_openbookqa
language:
- en
library_name: transformers
license: other
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/jahyungu/Falcon3-7B-Instruct_openbookqa
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Falcon3-7B-Instruct_openbookqa-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Falcon3-7B-Instruct_openbookqa-GGUF/resolve/main/Falcon3-7B-Instruct_openbookqa.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon3-7B-Instruct_openbookqa-GGUF/resolve/main/Falcon3-7B-Instruct_openbookqa.Q3_K_S.gguf) | Q3_K_S | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon3-7B-Instruct_openbookqa-GGUF/resolve/main/Falcon3-7B-Instruct_openbookqa.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Falcon3-7B-Instruct_openbookqa-GGUF/resolve/main/Falcon3-7B-Instruct_openbookqa.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon3-7B-Instruct_openbookqa-GGUF/resolve/main/Falcon3-7B-Instruct_openbookqa.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon3-7B-Instruct_openbookqa-GGUF/resolve/main/Falcon3-7B-Instruct_openbookqa.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Falcon3-7B-Instruct_openbookqa-GGUF/resolve/main/Falcon3-7B-Instruct_openbookqa.Q4_K_M.gguf) | Q4_K_M | 4.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Falcon3-7B-Instruct_openbookqa-GGUF/resolve/main/Falcon3-7B-Instruct_openbookqa.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon3-7B-Instruct_openbookqa-GGUF/resolve/main/Falcon3-7B-Instruct_openbookqa.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon3-7B-Instruct_openbookqa-GGUF/resolve/main/Falcon3-7B-Instruct_openbookqa.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Falcon3-7B-Instruct_openbookqa-GGUF/resolve/main/Falcon3-7B-Instruct_openbookqa.Q8_0.gguf) | Q8_0 | 8.0 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Falcon3-7B-Instruct_openbookqa-GGUF/resolve/main/Falcon3-7B-Instruct_openbookqa.f16.gguf) | f16 | 15.0 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
vendi11/blockassist-bc-placid_placid_llama_1757065073
|
vendi11
| 2025-09-05T09:38:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T09:38:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
niotyere/blockassist-bc-sizable_leggy_finch_1757065036
|
niotyere
| 2025-09-05T09:37:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sizable leggy finch",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T09:37:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sizable leggy finch
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hamedkharazmi/blockassist-bc-tough_webbed_hamster_1757060826
|
hamedkharazmi
| 2025-09-05T09:36:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tough webbed hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T09:36:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tough webbed hamster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vomqal/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-masked_snappy_caribou
|
vomqal
| 2025-09-05T09:36:44Z | 14 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am masked_snappy_caribou",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-03T00:27:47Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am masked_snappy_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]
- **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]
|
Meet-Kadam/finetuned-lora-resume-parser-v1
|
Meet-Kadam
| 2025-09-05T09:25:42Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:mistralai/mathstral-7b-v0.1",
"lora",
"transformers",
"text-generation",
"conversational",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-09-04T12:02:30Z |
---
library_name: peft
license: apache-2.0
base_model: mistralai/mathstral-7b-v0.1
tags:
- base_model:adapter:mistralai/mathstral-7b-v0.1
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: finetuned-lora-resume-parser-v1
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. -->
# finetuned-lora-resume-parser-v1
This model is a fine-tuned version of [mistralai/mathstral-7b-v0.1](https://huggingface.co/mistralai/mathstral-7b-v0.1) 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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: 50
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.17.1
- Transformers 4.56.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
|
alvarobartt/jina-code-embeddings-1.5b
|
alvarobartt
| 2025-09-05T09:22:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"feature-extraction",
"mteb",
"sentence-transformers",
"text-embeddings-inference",
"arxiv:2508.21290",
"base_model:Qwen/Qwen2.5-Coder-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Coder-1.5B",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
feature-extraction
| 2025-09-05T08:24:48Z |
---
base_model:
- Qwen/Qwen2.5-Coder-1.5B
license: cc-by-nc-4.0
tags:
- feature-extraction
- mteb
- sentence-transformers
- text-embeddings-inference
inference: false
library_name: transformers
pipeline_tag: feature-extraction
---
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>The code embedding model trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
# Jina Code Embeddings: A Small but Performant Code Embedding Model
## Intended Usage & Model Info
`jina-code-embeddings` is an embedding model for code retrieval.
The model supports various types of code retrieval (text-to-code, code-to-code, code-to-text, code-to-completion) and technical question answering across 15+ programming languages.
Built on [Qwen/Qwen2.5-Coder-1.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B), `jina-code-embeddings-1.5b` features:
- **Multilingual support** (15+ programming languages) and compatibility with a wide range of domains, including web development, software development, machine learning, data science, and educational coding problems.
- **Task-specific instruction prefixes** for NL2Code, Code2Code, Code2NL, Code2Completion, and Technical QA, which can be selected at inference time.
- **Flexible embedding size**: dense embeddings are 1536-dimensional by default but can be truncated to as low as 128 with minimal performance loss.
Summary of features:
| Feature | Jina Code Embeddings 1.5B |
|------------|------------|
| Base Model | Qwen2.5-Coder-1.5B |
| Supported Tasks | `nl2code`, `code2code`, `code2nl`, `code2completion`, `qa` |
| Model DType | BFloat 16 |
| Max Sequence Length | 32768 |
| Embedding Vector Dimension | 1536 |
| Matryoshka dimensions | 128, 256, 512, 1024, 1536 |
| Pooling Strategy | Last-token pooling |
| Attention Mechanism | FlashAttention2 |
## Usage
<details>
<summary>Requirements</a></summary>
The following Python packages are required:
- `transformers>=4.53.0`
- `torch>=2.7.1`
### Optional / Recommended
- **flash-attention**: Installing [flash-attention](https://github.com/Dao-AILab/flash-attention) is recommended for improved inference speed and efficiency, but not mandatory.
- **sentence-transformers**: If you want to use the model via the `sentence-transformers` interface, install this package as well.
</details>
<details>
<summary>via <a href="https://huggingface.co/docs/transformers/en/index">transformers</a></summary>
```python
# !pip install transformers>=4.53.0 torch>=2.7.1
import torch
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer
INSTRUCTION_CONFIG = {
"nl2code": {
"query": "Find the most relevant code snippet given the following query:\n",
"passage": "Candidate code snippet:\n"
},
"qa": {
"query": "Find the most relevant answer given the following question:\n",
"passage": "Candidate answer:\n"
},
"code2code": {
"query": "Find an equivalent code snippet given the following code snippet:\n",
"passage": "Candidate code snippet:\n"
},
"code2nl": {
"query": "Find the most relevant comment given the following code snippet:\n",
"passage": "Candidate comment:\n"
},
"code2completion": {
"query": "Find the most relevant completion given the following start of code snippet:\n",
"passage": "Candidate completion:\n"
}
}
MAX_LENGTH = 8192
def cosine_similarity(x,y):
x = F.normalize(x, p=2, dim=1)
y = F.normalize(y, p=2, dim=1)
return x @ y.T
def last_token_pool(last_hidden_states, attention_mask):
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def add_instruction(instruction, query):
return f'{instruction}{query}'
# The queries and documents to embed
queries = [
add_instruction(INSTRUCTION_CONFIG["nl2code"]["query"], "print hello world in python"),
add_instruction(INSTRUCTION_CONFIG["nl2code"]["query"], "initialize array of 5 zeros in c++")
]
documents = [
add_instruction(INSTRUCTION_CONFIG["nl2code"]["passage"], "print('Hello World!')"),
add_instruction(INSTRUCTION_CONFIG["nl2code"]["passage"], "int arr[5] = {0, 0, 0, 0, 0};")
]
all_inputs = queries + documents
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-code-embeddings-1.5b')
model = AutoModel.from_pretrained('jinaai/jina-code-embeddings-1.5b')
batch_dict = tokenizer(
all_inputs,
padding=True,
truncation=True,
max_length=MAX_LENGTH,
return_tensors="pt",
)
batch_dict.to(model.device)
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
query_embeddings = embeddings[:2]
passage_embeddings = embeddings[2:]
# Compute the (cosine) similarity between the query and document embeddings
scores = cosine_similarity(query_embeddings, passage_embeddings)
print(scores)
# tensor([[0.7647, 0.1115],
# [0.0930, 0.6606]], grad_fn=<MmBackward0>)
```
</details>
<details>
<summary>via <a href="https://sbert.net/">sentence-transformers</a></summary>
```python
# !pip install sentence_transformers>=5.0.0 torch>=2.7.1
import torch
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer(
"jinaai/jina-code-embeddings-1.5b",
model_kwargs={
"torch_dtype": torch.bfloat16,
"attn_implementation": "flash_attention_2",
"device_map": "cuda"
},
tokenizer_kwargs={"padding_side": "left"},
)
# The queries and documents to embed
queries = [
"print hello world in python",
"initialize array of 5 zeros in c++"
]
documents = [
"print('Hello World!')",
"int arr[5] = {0, 0, 0, 0, 0};"
]
query_embeddings = model.encode(queries, prompt_name="nl2code_query")
document_embeddings = model.encode(documents, prompt_name="nl2code_document")
# Compute the (cosine) similarity between the query and document embeddings
similarity = model.similarity(query_embeddings, document_embeddings)
print(similarity)
# tensor([[0.7670, 0.1117],
# [0.0938, 0.6607]])
```
</details>
<details>
<summary>via <a href="https://github.com/vllm-project/vllm">vLLM</a></summary>
```python
import torch
import torch.nn.functional as F
from vllm import LLM
INSTRUCTION_CONFIG = {
"nl2code": {
"query": "Find the most relevant code snippet given the following query:\n",
"passage": "Candidate code snippet:\n"
},
"qa": {
"query": "Find the most relevant answer given the following question:\n",
"passage": "Candidate answer:\n"
},
"code2code": {
"query": "Find an equivalent code snippet given the following code snippet:\n",
"passage": "Candidate code snippet:\n"
},
"code2nl": {
"query": "Find the most relevant comment given the following code snippet:\n",
"passage": "Candidate comment:\n"
},
"code2completion": {
"query": "Find the most relevant completion given the following start of code snippet:\n",
"passage": "Candidate completion:\n"
}
}
def add_instruction(instruction, text):
return f"{instruction}{text}"
def cosine_similarity(x, y):
x = F.normalize(x, p=2, dim=1)
y = F.normalize(y, p=2, dim=1)
return x @ y.T
# Build the queries and documents
queries = [
add_instruction(INSTRUCTION_CONFIG["nl2code"]["query"], "print hello world in python"),
add_instruction(INSTRUCTION_CONFIG["nl2code"]["query"], "initialize array of 5 zeros in c++"),
]
documents = [
add_instruction(INSTRUCTION_CONFIG["nl2code"]["passage"], "print('Hello World!')"),
add_instruction(INSTRUCTION_CONFIG["nl2code"]["passage"], "int arr[5] = {0, 0, 0, 0, 0};"),
]
all_inputs = queries + documents
# vLLM embedding model
llm = LLM(
model="jinaai/jina-code-embeddings-1.5b",
task="embed"
)
# Encode with vLLM
outputs = llm.encode(all_inputs)
# Collect embeddings into a single tensor
emb_list = []
for out in outputs:
vec = out.outputs.data.detach()
emb_list.append(vec)
embeddings = torch.stack(emb_list, dim=0)
# Split into query and passage embeddings
n_q = len(queries)
query_embeddings = embeddings[:n_q]
passage_embeddings = embeddings[n_q:]
# Cosine similarity matrix (queries x documents)
scores = cosine_similarity(query_embeddings, passage_embeddings)
print(scores)
# tensor([[0.7650, 0.1118],
# [0.0937, 0.6613]])
```
</details>
## Citation
Please refer to our [technical report of jina-code-embeddings](https://arxiv.org/abs/2508.21290) for training details and benchmarks. If you find it useful in your research, please cite the following paper:
```
@misc{kryvosheieva2025efficientcodeembeddingscode,
title={Efficient Code Embeddings from Code Generation Models},
author={Daria Kryvosheieva and Saba Sturua and Michael Günther and Scott Martens and Han Xiao},
year={2025},
eprint={2508.21290},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.21290},
}
```
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
|
Rudra-madlads/blockassist-bc-jumping_swift_gazelle_1757063976
|
Rudra-madlads
| 2025-09-05T09:20:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"jumping swift gazelle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T09:20:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- jumping swift gazelle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1757063582
|
arif696
| 2025-09-05T09:15:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T09:14:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
whizwang/blockassist-bc-amphibious_roaring_koala_1757063620
|
whizwang
| 2025-09-05T09:15:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious roaring koala",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T09:14:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious roaring koala
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arunav007/arunav_flux
|
arunav007
| 2025-09-05T09:11:07Z | 108 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-24T06:03:43Z |
---
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
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: ARUNAV
---
# Arunav_Flux
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `ARUNAV` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ARUNAV",
"lora_weights": "https://huggingface.co/arunav007/arunav_flux/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('arunav007/arunav_flux', weight_name='lora.safetensors')
image = pipeline('ARUNAV').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2002
- Learning rate: 0.0004
- LoRA rank: 20
## Contribute your own examples
You can use the [community tab](https://huggingface.co/arunav007/arunav_flux/discussions) to add images that show off what you’ve made with this LoRA.
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1757061522
|
kojeklollipop
| 2025-09-05T09:08:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T09:08:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Rudra-madlads/blockassist-bc-jumping_swift_gazelle_1757062750
|
Rudra-madlads
| 2025-09-05T09:00:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"jumping swift gazelle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T08:59:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- jumping swift gazelle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
NahedDom/blockassist-bc-flapping_stocky_leopard_1757060711
|
NahedDom
| 2025-09-05T08:59:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T08:59:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping stocky leopard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Mildbutterchicken/POVMIS
|
Mildbutterchicken
| 2025-09-05T08:54:43Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:Qwen/Qwen-Image",
"base_model:adapter:Qwen/Qwen-Image",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2025-09-05T08:51:27Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/Screen Shot 2025-09-03 at 9.48.15 pm.png
text: Screenshot
base_model: Qwen/Qwen-Image
instance_prompt: POV
license: apache-2.0
---
# POVMIS
<Gallery />
## Trigger words
You should use `POV` to trigger the image generation.
## Download model
[Download](/Mildbutterchicken/POVMIS/tree/main) them in the Files & versions tab.
|
Muapi/glock-17-g17-gen-4-gun
|
Muapi
| 2025-09-05T08:51:27Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-05T08:51:14Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Glock 17 (G17) Gen 4 - Gun
**Base model**: Flux.1 D
**Trained words**: G17Model Glock Pistol
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:989921@1109023", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/ob-cute-hand-drawn-illustrations
|
Muapi
| 2025-09-05T08:50:57Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-05T08:50:34Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# OB俏皮手绘插画Cute hand-drawn illustrations
**Base model**: Flux.1 D
**Trained words**: OBqpsh
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1039590@1166237", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
QuantTrio/KAT-V1-40B-AWQ
|
QuantTrio
| 2025-09-05T08:50:49Z | 17 | 1 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"AWQ",
"量化修复",
"vLLM",
"conversational",
"arxiv:2507.08297",
"base_model:Kwaipilot/KAT-V1-40B",
"base_model:quantized:Kwaipilot/KAT-V1-40B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2025-08-22T09:26:45Z |
---
library_name: transformers
license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- AWQ
- 量化修复
- vLLM
base_model:
- Kwaipilot/KAT-V1-40B
base_model_relation: quantized
---
# KAT-V1-40B-AWQ
Base model: [Kwaipilot/KAT-V1-40B](https://huggingface.co/Kwaipilot/KAT-V1-40B)
### 【vLLM Single Node with 4 GPUs Startup Command】
```
CONTEXT_LENGTH=32768
vllm serve \
QuantTrio/KAT-V1-40B-AWQ \
--served-model-name KAT-V1-40B-AWQ \
--swap-space 16 \
--max-num-seqs 512 \
--max-model-len $CONTEXT_LENGTH \
--max-seq-len-to-capture $CONTEXT_LENGTH \
--gpu-memory-utilization 0.9 \
--tensor-parallel-size 4 \
--trust-remote-code \
--disable-log-requests \
--host 0.0.0.0 \
--port 8000
```
### 【Dependencies】
```
vllm==0.10.0
```
### 【Model Update Date】
```
2025-07-31
1. fast commit
```
### 【Model Files】
| File Size | Last Updated |
|--------|--------------|
| `22GB` | `2025-07-31` |
### 【Model Download】
```python
from huggingface_hub import snapshot_download
snapshot_download('QuantTrio/KAT-V1-40B-AWQ', cache_dir="your_local_path")
```
### 【Overview】
<div align="center">
<img src="https://raw.githubusercontent.com/Anditty/OASIS/refs/heads/main/Group.svg" width="60%" alt="Kwaipilot" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://huggingface.co/Kwaipilot/KAT-V1-40B" target="_blank">
<img alt="Hugging Face" src="https://img.shields.io/badge/HuggingFace-fcd022?style=for-the-badge&logo=huggingface&logoColor=000&labelColor"/>
</a>
<a href="https://arxiv.org/pdf/2507.08297" target="_blank">
<img alt="arXiv" src="https://img.shields.io/badge/arXiv-2507.08297-b31b1b.svg?style=for-the-badge"/>
</a>
</div>
# News
- Kwaipilot-AutoThink ranks first among all open-source models on [LiveCodeBench Pro](https://livecodebenchpro.com/), a challenging benchmark explicitly designed to prevent data leakage, and even surpasses strong proprietary systems such as Seed and o3-mini.
***
# Introduction
**KAT (Kwaipilot-AutoThink)** is an open-source large-language model that mitigates *over-thinking* by learning **when** to produce explicit chain-of-thought and **when** to answer directly.
Its development follows a concise two-stage training pipeline:
<table>
<thead>
<tr>
<th style="text-align:left; width:18%;">Stage</th>
<th style="text-align:left;">Core Idea</th>
<th style="text-align:left;">Key Techniques</th>
<th style="text-align:left;">Outcome</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>1. Pre-training</strong></td>
<td>Inject knowledge while separating “reasoning” from “direct answering”.</td>
<td>
<em>Dual-regime data</em><br>
• <strong>Think-off</strong> queries labeled via a custom tagging system.<br>
• <strong>Think-on</strong> queries generated by a multi-agent solver.<br><br>
<em>Knowledge Distillation + Multi-Token Prediction</em> for fine-grained utility.
</td>
<td>Base model attains strong factual and reasoning skills without full-scale pre-training costs.</td>
</tr>
<tr>
<td><strong>2. Post-training</strong></td>
<td>Make reasoning optional and efficient.</td>
<td>
<em>Cold-start AutoThink</em> — majority vote sets the initial thinking mode.<br>
<em>Step-SRPO</em> — intermediate supervision rewards correct <strong>mode selection</strong> and <strong>answer accuracy</strong> under that mode.
</td>
<td>Model triggers CoT only when beneficial, reducing token use and speeding inference.</td>
</tr>
</tbody>
</table>
***
# Data Format
KAT produces responses in a **structured template** that makes the reasoning path explicit and machine-parsable.
Two modes are supported:
## Special Tokens
| Token | Description |
|-------|-------------|
| `<judge>` | Analyzes the input to decide whether explicit reasoning is needed. |
| `<think_on>` / `<think_off>` | Indicates whether reasoning is **activated** (“on”) or **skipped** (“off”). |
| `<think>` | Marks the start of the chain-of-thought segment when `think_on` is chosen. |
| `<answer>` | Marks the start of the final user-facing answer. |
***
# 🔧 Quick Start
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "Kwaipilot/KAT-V1-40B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=65536,
temperature=0.6,
top_p=0.95,
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")
print("prompt:\n", prompt)
print("content:\n", content)
"""
prompt:
Give me a short introduction to large language model.
content:
<judge>
The user's request is to provide a concise factual introduction to large language models, which involves retrieving and summarizing basic information. This task is straightforward as it only requires recalling and presenting well-known details without deeper analysis. No complex reasoning is needed here—just a simple explanation will suffice.
</judge>
<think_off>
<answer>
A **Large Language Model (LLM)** is an advanced AI system trained on vast amounts of text data to understand, generate, and process human-like language. Here’s a concise introduction:
### Key Points:
1. **Training**: Trained on diverse text sources (books, websites, etc.) using deep learning.
2. **Capabilities**:
- Answer questions, generate text, summarize content, translate languages.
- Understand context, sentiment, and nuances in language.
3. **Architecture**: Often based on **transformer models** (e.g., BERT, GPT, LLaMA).
4. **Scale**: Billions of parameters, requiring massive computational resources.
5. **Applications**: Chatbots, content creation, coding assistance, research, and more.
### Examples:
- **OpenAI’s GPT-4**: Powers ChatGPT.
- **Google’s Gemini**: Used in Bard.
- **Meta’s LLaMA**: Open-source alternative.
### Challenges:
- **Bias**: Can reflect biases in training data.
- **Accuracy**: May hallucinate "facts" not grounded in reality.
- **Ethics**: Raises concerns about misinformation and job displacement.
LLMs represent a leap forward in natural language processing, enabling machines to interact with humans in increasingly sophisticated ways. 🌐🤖
</answer>
"""
```
***
# Future Releases
Looking ahead, we will publish a companion paper that fully documents the **AutoThink training framework**, covering:
* Cold-start initialization procedures
* Reinforcement-learning (Step-SRPO) strategies
* Data curation and reward design details
At the same time, we will open-source:
* **Training resources** – the curated dual-regime datasets and RL codebase
* **Model suite** – checkpoints at 1.5B, 7B, and 13B parameters, all trained with AutoThink gating
|
Muapi/retro-future-dystopia-flux-lora
|
Muapi
| 2025-09-05T08:49:50Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-05T08:49:40Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Retro Future Dystopia - Flux Lora
**Base model**: Flux.1 D
**Trained words**: RetroFutureDystopia
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:886913@992798", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.