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README.md

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+---
+language:
+  - ja
+tags:
+  - vision-language-model
+  - medical
+  - japanese
+  - radiology
+  - reasoning
+  - llava
+license: other
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
+
+# [Med-Asagi]
+
+[Med-Asagi] は、日本語の医療画像理解を目的として構築した大規模視覚言語モデル（VLM）です。  
+医療画像と日本語テキストを統合して扱うことができ、画像に対する質問応答や読影支援のようなタスクに利用できます。
+
+本モデルは、医療分野で利用しやすい**オープンな日本語医療 VLM**の実現を目的として開発されました。ベースアーキテクチャには LLaVA 系の構成を採用し、画像エンコーダに SigLIP、日本語医療 LLM をテキストデコーダとして組み合わせています。総パラメータ数は約 14B です。
+詳しくは論文を参照ください。
+
+言語処理学会の投稿後にモデルの改良（及び公開可能モデルに変更）を行ったため、発表した内容とは異なっています。変更点は以下の３つです。
+- Image encoderをgoogle/siglip2-so400m-patch16-512に変更
+- LLMをSIP-med-LLM/SIP-jmed-llm-3-13b-OP-4k-baseに変更
+- 学習ステージを3段階に変更（Stage2として、Enriched captionを新たに学習）
+
+## モデルの特徴
+
+- 日本語の医療画像タスクに特化した視覚言語モデル
+- 約 1,200 万件規模の日本語医療学習データを用いて学習
+- 推論過程を明示的に出力する **Reasoning / CoT 形式** の学習データで訓練
+- CT 画像および胸部 X 線画像の評価で、既存のオープン VLM と比較して高い性能を確認
+
+本研究では、通常の応答のみを生成するモデルに加えて、`<description>` と `<thinking>` を含む推論過程を出力するモデルを構築しています。ここで公開しているのは **推論ありモデル** です。
+
+## 学習データ
+
+日本語の医療画像テキスト対データは公開規模が限られているため、本モデルでは既存の英語医療データをもとに、日本語の大規模学習データを構築しました。
+全学習データはMIL-UT/Japanese-Medical-VQA-12mにて公開しています。
+
+最終的に、Stage1, 2 用のキャプションデータとして **12,125,556 件**、Stage3 用の VQA データとして **12,125,203 件**を用いています。
+
+## モデル構成
+
+本モデルは LLaVA ベースのエンコーダ・デコーダ型 VLM です。
+
+| Model components|  Model                                    | Parameters            | 
+| --------------- | ----------------------------------------- | --------------------- |
+| Image encoder   | google/siglip2-so400m-patch16-512         | 428,772,800           |
+| Projectpr       | 2層 MLP                                   | 64,240,640            | 
+| LLM             | SIP-med-LLM/SIP-jmed-llm-3-13b-OP-4k-base | 13,707,924,480        | 
+| 合計             |                                           | 14,200,937,920        |
+
+
+学習は *3段階*で行っています。
+
+| Stage           |  学習対象                                  | 訓練データ                | 
+| --------------- | ----------------------------------------- | ----------------------- |
+| Stage1          | プロジェクター層                             | Image + Caption         |
+| Stage2          | プロジェクター層, テキストデコーダ              | Image + Enriched caption| 
+| Stage3          | プロジェクター層, テキストデコーダ              | Image + VQA             | 
+
+
+学習には Megatron-LM を用いました。
+
+## 評価結果
+
+本モデルは、以下の 2 種類の評価で有効性を確認しています。
+
+- **CT 画像に対する日本語 VQA**
+- **胸部 X 線画像に対する日本語読影レポート生成**
+
+LLM as a Judgeの評価については医療専門家のフィードバックを受けてプロンプトを改変しており、言語処理学会で発表した内容と異なります。（後日全ての結果を更新予定）
+
+| MODEL |  LLM as a Judge   |
+| GPT 5 | 4.05 |
+| GPT 4.1 | 3.18 |
+| Med-Asagi | 3.40 |
+| InternVL | 2.80 |
+| Vila_jp | 2.76 |
+
+
+## 出力例
+
+後日追記
+
+## 使用方法
+
+後日追記
+
+
+## 使用上の注意
+
+* 実運用の診療支援に利用する場合は、医師等の専門家による確認が必須です。
+* 学習データや評価データには限界があり、誤った推論や不正確な説明を生成する可能性があります。
+* 推論過程として出力される内容が、必ずしも医学的に正しいとは限りません。
+
+## ライセンス
+
+cc-by-sa-4.0
+
+## 引用
+
+本モデルを利用した場合は、以下の論文を引用してください。
+
+```bibtex
+@inproceedings{ando2026japanese_medical_vlm,
+  title={医療用大規模日本語視覚言語モデルの構築},
+  author={安道 健一郎 and 黒瀬 優介 and 菊地 智博 and 牧元 久樹 and 小寺 聡 and 小林 和馬 and 合田 和生 and 村尾 晃平 and 吉田 浩 and 田村 孝之 and 合田 憲人 and 喜連川 優 and 原田 達也},
+  booktitle={言語処理学会第32回年次大会},
+  year={2026}
+}
+```
+
+## メンテナンス情報
+
+問い合わせ先: ando \[at\] mi.t.u-tokyo.ac.jp へご連絡ください。

config.json

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+{
+  "architectures": [
+    "LlavaForConditionalGeneration"
+  ],
+  "auto_map": {
+    "AutoConfig": "MIL-UT/Asagi-14B--configuration_llava.LlavaConfig",
+    "AutoModel": "MIL-UT/Asagi-14B--modeling_llava.LlavaForConditionalGeneration"
+  },
+  "ignore_index": -100,
+  "image_seq_length": 576,
+  "image_token_index": 99574,
+  "model_type": "llava",
+  "multimodal_projector_bias": true,
+  "pad_token_id": 99575,
+  "projector_hidden_act": "gelu",
+  "projector_hidden_size": 10240,
+  "text_config": {
+    "_name_or_path": "SIP-med-LLM/SIP-jmed-llm-3-13b-OP-4k-base",
+    "architectures": [
+      "LlamaForCausalLM"
+    ],
+    "hidden_size": 5120,
+    "intermediate_size": 13824,
+    "max_position_embeddings": 4096,
+    "model_type": "llama",
+    "num_attention_heads": 40,
+    "num_hidden_layers": 40,
+    "num_key_value_heads": 40,
+    "rms_norm_eps": 1e-05,
+    "torch_dtype": "bfloat16",
+    "vocab_size": 99584
+  },
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.1",
+  "vision_config": {
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "image_size": 512,
+    "intermediate_size": 4304,
+    "layer_norm_eps": 1e-06,
+    "model_type": "siglip_vision_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 27,
+    "patch_size": 16,
+    "vocab_size": 99584
+  },
+  "vision_feature_layer": -2,
+  "vision_feature_select_strategy": "default",
+  "vocab_size": 99584
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 99575,
+  "transformers_version": "4.45.1"
+}

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preprocessor_config.json

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+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
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+  "size": {
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+    "width": 512
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+}

processor_config.json

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+  "num_additional_image_tokens": 0,
+  "patch_size": 16,
+  "processor_class": "LlavaProcessor",
+  "vision_feature_select_strategy": "default"
+}

special_tokens_map.json

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+    "rstrip": false,
+    "single_word": false
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+}

text_config.json

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+  "use_cache": true,
+  "vocab_size": 99584
+}

tokenizer_config.json

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+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<CLS|LLM-jp>",
+  "eod_token": "</s>",
+  "eos_token": "</s>",
+  "extra_ids": 0,
+  "extra_special_tokens": {},
+  "mask_token": "<MASK|LLM-jp>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "processor_class": "SiglipProcessor",
+  "sep_token": "<SEP|LLM-jp>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "<unk>"
+}