Update README.md

README.md

@@ -1,89 +1,184 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+datasets:
+- isek-ai/danbooru-tags-2024
+base_model: p1atdev/dart-v2-moe-base
+tags:
+- trl
+- sft
+- optimum
+- danbooru
+inference: false
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Dart (Danbooru Tags Transformer) v2
+
+This model is a fine-tuned Dart (Danbooru Tags Transformer) model that generates danbooru tags.
+
+Demo: [🤗 Space with ZERO](https://huggingface.co/spaces/p1atdev/danbooru-tags-transformer-v2)
+
+## Model variants
+
+|Name|Architecture|Param size|Type|
+|-|-|-|-|
+|[v2-moe-sft](https://huggingface.co/p1atdev/dart-v2-moe-sft)|Mixtral|166m|SFT|
+|[v2-moe-base](https://huggingface.co/p1atdev/dart-v2-moe-base)|Mixtral|166m|Pretrain|
+|[v2-sft](https://huggingface.co/p1atdev/dart-v2-sft)|Mistral|114m|SFT|
+|[v2-base](https://huggingface.co/p1atdev/dart-v2-base)|Mistral|114m|Pretrain|
+|[v2-vectors](https://huggingface.co/p1atdev/dart-v2-vectors)|Embedding|-|Tag Embedding|
+
+## Usage
+
+### Using 🤗Transformers
+
+```py
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+MODEL_NAME = "p1atdev/dart-v2-sft"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.bfloat16)
+
+prompt = (
+    f"<|bos|>"
+    f"<copyright>vocaloid</copyright>"
+    f"<character>hatsune miku</character>"
+    f"<|rating:general|><|aspect_ratio:tall|><|length:long|>"
+    f"<general>1girl, cat ears<|identity:none|><|input_end|>"
+)
+inputs = tokenizer(prompt, return_tensors="pt").input_ids
+
+with torch.no_grad():
+  outputs = model.generate(
+    inputs,
+    do_sample=True,
+    temperature=1.0,
+    top_p=1.0,
+    top_k=100,
+    max_new_tokens=128,
+    num_beams=1,
+  )
+
+print(", ".join([tag for tag in tokenizer.batch_decode(outputs[0], skip_special_tokens=True) if tag.strip() != ""]))
+# vocaloid, hatsune miku, 1girl, cat ears, closed mouth, detached sleeves, dress, expressionless, from behind, full body, green theme, hair ornament, hair ribbon, headphones, high heels, holding, holding microphone, long hair, microphone, monochrome, necktie, ribbon, short dress, shoulder tattoo, simple background, sleeveless, sleeveless dress, spot color, standing, tattoo, thighhighs, twintails, very long hair, white background
+```
+
+### Using 📦`dartrs` library
+
+> [!WARNING]
+> This library is very experimental and there will be breaking changes in the future.
+
+[📦`dartrs`](https://github.com/p1atdev/dartrs) is a [🤗`candle`](https://github.com/huggingface/candle) backend inference library for Dart v2 models.
+
+```py
+pip install -U dartrs
+```
+
+```py
+from dartrs.dartrs import DartTokenizer
+from dartrs.utils import get_generation_config
+from dartrs.v2 import (
+    compose_prompt,
+    MixtralModel,
+    V2Model,
+)
+import time
+import os
+
+MODEL_NAME = "p1atdev/dart-v2-sft"
+
+model = MixtralModel.from_pretrained(MODEL_NAME)
+tokenizer = DartTokenizer.from_pretrained(MODEL_NAME)
+
+config = get_generation_config(
+    prompt=compose_prompt(
+        copyright="vocaloid",
+        character="hatsune miku",
+        rating="general", # sfw, general, sensitive, nsfw, questionable, explicit
+        aspect_ratio="tall", # ultra_wide, wide, square, tall, ultra_tall
+        length="medium", # very_short, short, medium, long, very_long
+        identity="none", # none, lax, strict
+        prompt="1girl, cat ears",
+    ),
+    tokenizer=tokenizer,
+)
+
+start = time.time()
+output = model.generate(config)
+end = time.time()
+
+print(output)
+print(f"Time taken: {end - start:.2f}s")
+# cowboy shot, detached sleeves, empty eyes, green eyes, green hair, green necktie, hair in own mouth, hair ornament, letterboxed, light frown, long hair, long sleeves, looking to the side, necktie, parted lips, shirt, sleeveless, sleeveless shirt, twintails, wing collar
+# Time taken: 0.26s
+```
+
+## Prompt Format
+
+```py
+prompt = (
+    f"<|bos|>"
+    f"<copyright>{copyright_tags_here}</copyright>"
+    f"<character>{character_tags_here}</character>"
+    f"<|rating:general|><|aspect_ratio:tall|><|length:long|>"
+    f"<general>{general_tags_here}<|identity:none|><|input_end|>"
+)
+```
+
+- Rating tag: `<|rating:sfw|>`, `<|rating:general|>`, `<|rating:sensitive|>`, `nsfw`, `<|rating:questionable|>`, `<|rating:explicit|>`
+  - `sfw`: randomly generates tags in `general` or `sensitive` rating categories.
+  - `general`: generates tags in `general` rating category.
+  - `sensitive`: generates tags in `sensitive` rating category.
+  - `nsfw`: randomly generates tags in `questionable` or `explicit` rating categories.
+  - `questionable`: generates tags in `questionable` rating category.
+  - `explicit`: generates tags in `explicit` rating category.
+
+- Aspect ratio tag: `<|aspect_ratio:ultra_wide|>`, `<|aspect_ratio:wide|>`, `<|aspect_ratio:square|>`, `<|aspect_ratio:tall|>`, `<|aspect_ratio:ultra_tall|>`
+  - `ultra_wide`: generates tags suits for extremely wide aspect ratio images. (~2:1)
+  - `wide`: generates tags suits for wide aspect ratio images. (2:1~9:8)
+  - `square`: generates tags suits for square aspect ratio images. (9:8~8:9)
+  - `tall`: generates tags suits for tall aspect ratio images. (8:9~1:2)
+  - `ultra_tall`: generates tags suits for extremely tall aspect ratio images. (1:2~)
+
+- Length tag: `<|length:very_short|>`, `<|length:short|>`, `<|length:medium|>`, `<|length:long|>`, `<|length:very_long|>`
+  - `very_short`: totally generates ~10 number of tags.
+  - `short`: totally generates ~20 number of tags.
+  - `medium`: totally generates ~30 number of tags.
+  - `long`: totally generates ~40 number of tags.
+  - `very_long`: totally generates 40~ number of tags.
+
+- **Identity tag**: `<|identity:none|>`, `<|identity:lax|>`, `<|identity:strict|>`
+  - This tag is 
+  - `none`: recommended if the specified general tags are very few. It generates tags very creatively, but sometimes ignores the condition of the general tags.
+  - `lax`: recommended if you want to keep the identity of charaacters or subjects in the general tags. This tag tries not to generate tags which conflict with the input general tags.
+  - `strict`: recommended if you strongly want to keep the identity of charaacters or subjects in the general tags. This tag tries not to generate tags which conflict with the input general tags more strictly than `lax`. But this is less creative, so if you don't like the result with `strict`, please try `lax` or `none`.
 
 ## 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. -->
+- **Developed by:** Plat
+- **Model type:** Causal language model
+- **Language(s) (NLP):** Danbooru tags
+- **License:** Apache-2.0
+- **Finetuned from model:** [dart-v2-moe-base](https://huggingface.co/p1atdev/dart-v2-moe-base)
+- **Demo:** Available on [🤗 Space](https://huggingface.co/spaces/p1atdev/danbooru-tags-transformer-v2)
 
-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. -->
+This model was trained with:
+
+- [isek-ai/danbooru-tags-2024](https://huggingface.co/datasets/isek-ai/danbooru-tags-2024/tree/202403-at20240423) with revision `202403-at20240423`: 7M size of danbooru tags dataset since 2005 to 2024/03/31.
 
-[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. -->
+TODO
 
 #### Preprocessing [optional]
 
@@ -92,110 +187,47 @@ Use the code below to get started with the model.
 
 #### 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. -->
+The following hyperparameters were used during training:
+- learning_rate: 0.00025
+- train_batch_size: 1024
+- eval_batch_size: 256
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 2048
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 1000
+- num_epochs: 4
 
-[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
-
+Evaluation has not been done yet and it needs to evaluate.
 
 
-## Model Examination [optional]
+#### Model Architecture and Objective 
 
-<!-- Relevant interpretability work for the model goes here -->
+The architecture of this model is [Mistral](https://huggingface.co/docs/transformers/model_doc/mistral). See details in [config.json](./config.json).
 
-[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]
+Server in a university laboratory
 
 #### Hardware
 
-[More Information Needed]
+8x RTX A6000
 
 #### 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]
+- Dataset processing: [🤗 Datasets](https://github.com/huggingface/datasets)
+- Training: [🤗 Transformers](https://github.com/huggingface/transformers)
+- SFT: [🤗 TRL](https://github.com/huggingface/trl)
+- Inference library: [📦 dartrs](https://github.com/p1atdev/dartrs)
+  - Backend: [🤗 candle](https://github.com/huggingface/candle)
 
+## Related Projects
 
+- [dart-v1](https://huggingface.co/p1atdev/dart-v1): The first version of the Dart model.
+- [KBlueLeaf/DanTagGen](https://huggingface.co/collections/KBlueLeaf/dantaggen-65f82fa9335881a67573556b): The Aspect Ratio tag was inspired by this project.
+- [furusu/danbooru-tag-similarity](https://huggingface.co/spaces/furusu/danbooru-tag-similarity): The idea of clustering tags and its training method was inspired by this project.