初始化项目，由ModelHub XC社区提供模型

Model: EQUES/MedLLama3-JP-v2
Source: Original Platform

.gitattributes

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README.md

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+---
+library_name: transformers
+tags:
+- medical
+- merge
+license: llama3
+language:
+- ja
+metrics:
+- accuracy
+---
+
+# Llama3ベースの日本語医療LLM MedLlama3-JP
+
+このモデルはLlama3の継続学習により作成された４種類のLLMから成るマージモデルです。
+日本語LLMをベースとし、英語の医療LLMをマージすることで日本語での医療知識や医療Q&Aへの回答力を獲得することを目的としています。
+
+- base :[tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1)
+- [aaditya/Llama3-OpenBioLLM-8B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B)
+- [Henrychur/MMed-Llama-3-8B](https://huggingface.co/Henrychur/MMed-Llama-3-8B)
+- [elyza/Llama-3-ELYZA-JP-8B](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B)
+
+
+医療目的には利用しないでください。  
+本モデルの出力に関してその正確性等を保証しません。
+
+
+# 評価
+
+IgakuQA（日本医師国家試験データセット）の正答率を用いて評価しました。  
+評価実験の設定は[IgakuQA](https://github.com/jungokasai/IgakuQA/tree/main/scripts)に従いました。  
+GPTモデルのパフォーマンスは[Kasai et al., 2023](https://arxiv.org/pdf/2303.18027)の結果に基づき算出しました。  
+
+
+| モデル | 　正答率 （全2485問） |
+| ---- | ---- |
+| [EQUES/MedLLama3-JP-v2](https://huggingface.co/EQUES/MedLLama3-JP-v2) | 46.6% |
+| [tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1) | 42.2% |
+| [elyza/Llama-3-ELYZA-JP-8B](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B) | 43.9% |
+| ---- | ---- |
+| GPT-4 | 78.2% | 
+| ChatGPT | 54.9% | 
+| GPT-3 | 42.1% | 
+
+
+また、セクションごとの正答率をプロットした図が以下です。マージモデルがマージ元のモデルの良い所取りをしている傾向が窺えます。
+
+
+![](img/figure.png)
+
+
+## Usage
+
+```
+pip install transformers vllm
+```
+
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+
+model_name = "EQUES/MedLLama3-JP-v2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+llm = LLM(
+    model=model_name,
+    tensor_parallel_size=1,
+)
+
+sampling_params = SamplingParams(
+    temperature=0.6, top_p=0.9, max_tokens=512, stop="<|eot_id|>"
+)
+
+message = [
+    {"role": "system", "content": <question>},
+    {"role": "user","content": <answer>},
+    {"role": "system", "content": <question>},
+]
+
+prompt = tokenizer.apply_chat_template(message, tokenize=False, add_generation_prompt=True)
+output = llm.generate(prompt, sampling_params)
+print(output[0].outputs[0].text)
+```
+
+## Bias, Risks, and Limitations
+
+The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.
+
+## Acknowledgement
+
+We acknowledge the developers of each base model.
+
+
+## How to Cite
+
+```
+@misc{MedLLama3-JP-v2,
+      title={EQUES/MedLLama3-JP-v2},
+      url={https://huggingface.co/EQUES/MedLLama3-JP-v2},
+      author={Issey Sukeda},
+      year={2024},
+}
+```

config.json

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+{
+  "_name_or_path": "medmerge2",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.42.2",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "transformers_version": "4.42.2"
+}

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