Update README.md

README.md

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 ---
 license: cc-by-sa-4.0
+datasets:
+- KBlueLeaf/danbooru2023-sqlite
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- not-for-all-audiences
+- art
+widget:
+- text: "quality: masterpiece\nrating: safe\nartist: <|empty|>\ncharacters: <|empty|>\ncopyrights: <|empty|>\naspect ratio: 1.0\ntarget: <|short|>\ngeneral: 1girl, solo, dragon girl, dragon horns, dragon tail<|input_end|>"
 ---
+
+# DanTagGen - delta (rev2)
+DanTagGen(Danbooru Tag Generator) is inspired from p1atdev's dart project.
+But with different arch, dataset, format and different training strategy.
+
+## Difference between versions
+- alpha: pretrain on 2M dataset, smaller batch size. Limited ability
+- beta: pretrain on 5.3M dataset, larger batch size. More stable, better ability with only a few information provided.
+- delta: pretrain on 7.2M dataset, larger batch size. Slightly underfit but better diversity. quality tag introduced.
+  - rev2: resumed from delta, same dataset, 2 more epoch.
+
+## Model arch
+This version of DTG is trained from scratch with 400M param LLaMA arch.(In my personal preference I will call it NanoLLaMA)
+Since it is llama arch. Theoritically it should be able to be used in any LLaMA inference interface.
+
+This repo also provided converted FP16 gguf model and quantized 8bit/6bit gguf models.
+Basically it is recommended to use llama.cpp or llama-cpp-python to run this model. Which will be very fast.
+
+## Format
+```python3
+prompt = f"""
+quality: {quality or '<|empty|>'}
+rating: {rating or '<|empty|>'}
+artist: {artist.strip() or '<|empty|>'}
+characters: {characters.strip() or '<|empty|>'}
+copyrights: {copyrights.strip() or '<|empty|>'}
+aspect ratio: {f"{aspect_ratio:.1f}" or '<|empty|>'}
+target: {'<|' + target + '|>' if target else '<|long|>'}
+general: {", ".join(special_tags)}, {general.strip().strip(",")}<|input_end|>
+"""
+```
+
+for example:
+```
+quality: masterpiece
+rating: safe
+artist: <|empty|>
+characters: <|empty|>
+copyrights: <|empty|>
+aspect ratio: 1.0
+target: <|short|>
+general: 1girl, solo, dragon girl, dragon horns, dragon tail<|input_end|>
+```
+
+And you may get something like:
+```
+rating: safe
+artist: <|empty|>
+characters: <|empty|>
+copyrights: <|empty|>
+aspect ratio: 1.0
+target: <|short|>
+general: 1girl, solo, dragon girl, dragon horns, dragon tail<|input_end|>open mouth, red eyes, long hair, pointy ears, tail, black hair, chinese clothes, simple background, dragon, hair between eyes, horns, china dress, dress, looking at viewer, breasts
+```
+
+## Dataset and Training
+I use the trainer I implemented in HakuPhi to run the training.
+with Total 12epoch on 7.2M data. This model have roughly 10~15B token seen.
+
+The dataset is exported by HakuBooru with my danbooru sqlite database. Use the percentile of fav_count on each rating to filter the data. (2M = top 25%, 5.3M = top 75%)
+
+## Utilities
+HF space: https://huggingface.co/spaces/KBlueLeaf/DTG-demo
+Demo for DTG + Kohaku XL Epsilon: https://huggingface.co/spaces/KBlueLeaf/This-Cute-Dragon-Girl-Doesnt-Exist
+SD-WebUI Extension: https://github.com/KohakuBlueleaf/z-a1111-sd-webui-dtg
+ComfyUI Node: https://github.com/toyxyz/a1111-sd-webui-dtg_comfyui