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Model: KBlueLeaf/DanTagGen-delta Source: Original Platform
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79
README.md
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README.md
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---
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license: cc-by-nc-sa-4.0
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datasets:
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- KBlueLeaf/danbooru2023-sqlite
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- not-for-all-audiences
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- art
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widget:
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- 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|>"
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---
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# DanTagGen - delta
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DanTagGen(Danbooru Tag Generator) is inspired from p1atdev's dart project.
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But with different arch, dataset, format and different training strategy.
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## Difference between versions
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alpha: pretrain on 2M dataset, smaller batch size. Limited ability
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beta: pretrain on 5.3M dataset, larger batch size. More stable, better ability with only a few information provided.
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delta: pretrain on 7.2M dataset, larger batch size. Slightly underfit but better diversity. quality tag introduced.
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## Model arch
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This version of DTG is trained from scratch with 400M param LLaMA arch.(In my personal preference I will call it NanoLLaMA)
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Since it is llama arch. Theoritically it should be able to be used in any LLaMA inference interface.
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This repo also provided converted FP16 gguf model and quantized 8bit/6bit gguf models.
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Basically it is recommended to use llama.cpp or llama-cpp-python to run this model. Which will be very fast.
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## Format
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```python3
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prompt = f"""
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quality: {quality or '<|empty|>'}
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rating: {rating or '<|empty|>'}
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artist: {artist.strip() or '<|empty|>'}
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characters: {characters.strip() or '<|empty|>'}
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copyrights: {copyrights.strip() or '<|empty|>'}
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aspect ratio: {f"{aspect_ratio:.1f}" or '<|empty|>'}
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target: {'<|' + target + '|>' if target else '<|long|>'}
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general: {", ".join(special_tags)}, {general.strip().strip(",")}<|input_end|>
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"""
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```
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for example:
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```
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quality: masterpiece
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rating: safe
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artist: <|empty|>
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characters: <|empty|>
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copyrights: <|empty|>
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aspect ratio: 1.0
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target: <|short|>
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general: 1girl, solo, dragon girl, dragon horns, dragon tail<|input_end|>
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```
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And you may get something like:
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```
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rating: safe
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artist: <|empty|>
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characters: <|empty|>
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copyrights: <|empty|>
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aspect ratio: 1.0
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target: <|short|>
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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
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```
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## Dataset and Training
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I use the trainer I implemented in HakuPhi to run the training.
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with 10epoch on 7.2M data. This model have roughly 10~15B token seen.
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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%)
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## Utilities
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HF space: https://huggingface.co/spaces/KBlueLeaf/DTG-demo
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Demo for DTG + Kohaku XL Epsilon: https://huggingface.co/spaces/KBlueLeaf/This-Cute-Dragon-Girl-Doesnt-Exist
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SD-WebUI Extension: https://github.com/KohakuBlueleaf/z-a1111-sd-webui-dtg
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ComfyUI Node: https://github.com/toyxyz/a1111-sd-webui-dtg_comfyui
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added_tokens.json
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added_tokens.json
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{
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"<|empty|>": 32001,
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"<|input_end|>": 32000,
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"<|long|>": 32004,
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"<|short|>": 32003,
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"<|very_long|>": 32005,
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"<|very_short|>": 32002
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}
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config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "mish",
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"max_position_embeddings": 2048,
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"model_type": "llama",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"num_key_value_heads": 16,
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"pad_token_id": 2,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.38.2",
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"use_cache": false,
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"vocab_size": 32006
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}
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generation_config.json
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generation_config.json
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{
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"use_cache": false
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}
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ggml-model-Q6_K.gguf
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ggml-model-Q6_K.gguf
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ggml-model-Q8_0.gguf
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ggml-model-f16.gguf
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model.safetensors
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38
special_tokens_map.json
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|input_end|>",
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"<|empty|>",
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"<|very_short|>",
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"<|short|>",
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"<|long|>",
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"<|very_long|>"
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}
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BIN
tokenizer.model
(Stored with Git LFS)
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BIN
tokenizer.model
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tokenizer_config.json
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"unk_token": "<unk>",
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"use_default_system_prompt": false
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}
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