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README.md

+# Baichuan-M3-235B
+## 论文
+[Modeling Clinical Inquiry for Reliable Medical Decision-Making](https://arxiv.org/abs/2602.06570)
+
+## 模型简介
+Baichuan-M3 是百川智能推出的全新一代医疗增强大语言模型，是继 Baichuan-M2 之后的重要里程碑。
+与以往主要聚焦于静态问答或表面角色扮演的方法不同，Baichuan-M3 经过专门训练，能够显式建模 临床决策过程，旨在提升模型在真实医疗场景中的可用性与可靠性。该模型并非仅生成“听起来合理”的答案，或频繁给出诸如“你应尽快就医”等模糊建议，而是被训练为能够 主动获取关键临床信息、构建连贯的医学推理路径，并 系统性地约束易产生幻觉的行为。
+具有以下的亮点：
+超越 GPT-5.2：在 HealthBench、HealthBench-Hard、幻觉评估和 SCAN-bench 等多项指标上全面超越 OpenAI 最新模型，树立医疗 AI 新的 SOTA。
+高保真临床问诊能力：唯一在 SCAN-bench 全部三个维度（临床问诊、实验室检查、诊断）均排名第一的模型。
+低幻觉率，高可靠性：通过 Fact-Aware RL，在无外部工具辅助的情况下，幻觉率低于 GPT-5.2。
+ 高效部署：W4 量化将内存占用降至原始的 26%；Gated Eagle3 推测解码实现 96% 的加速。
+
+## 环境依赖
+
+|     软件     |                      版本                      |
+| :----------: | :--------------------------------------------: |
+|     DTK      |                    26.04.2                     |
+|    python    |                    3.10.12                     |
+| transformers |                     4.57.6                     |
+|    torch     |   2.5.1+das.opt1.dtk2604.20260116.g78471bfd    |
+|  accelerate  |                     1.12.0                     |
+| torchvision  |   0.20.1+das.opt1.dtk2604.20260116.g65c66897   |
+|  flash_attn  |   2.6.1+das.opt1.dtk2604.20260128.g034ec12d    |
+|     vllm     | 0.11.0+das.opt1.rc2.dtk2604.20260128.g0bf89b0c | 
+
+推荐使用镜像：harbor.sourcefind.cn:5443/dcu/admin/base/vllm:0.11.0-ubuntu22.04-dtk26.04-0127-py3.10-20260129
+
+- 挂载地址`-v`根据实际模型情况修改
+
+```bash
+docker run -it \
+    --shm-size 200g \
+    --network=host \
+    --name baichuan_m3 \
+    --privileged \
+    --device=/dev/kfd \
+    --device=/dev/dri \
+    --device=/dev/mkfd \
+    --group-add video \
+    --cap-add=SYS_PTRACE \
+    --security-opt seccomp=unconfined \
+    -u root \
+    -v /opt/hyhal/:/opt/hyhal/:ro \
+    -v /path/your_code_data/:/path/your_code_data/ \
+    harbor.sourcefind.cn:5443/dcu/admin/base/vllm:0.11.0-ubuntu22.04-dtk26.04-0127-py3.10-20260129 bash
+```
+更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用。
+关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装。
+
+
+## 数据集
+
+`暂无`
+
+## 训练
+
+`暂无`
+
+## 推理
+
+### vllm
+#### 单机推理
+需要
+```bash
+vllm serve /path/to/baichuan-inc/Baichuan-M3-235B --tensor-parallel-size 8 --max-model-len 8192 --gpu-memory-utilization 0.9 --served-model-name baichuan-m3 --reasoning-parser deepseek_r1
+```
+
+启动完成后可通过以下方式访问：
+```bash
+curl http://localhost:8000/v1/chat/completions   \
+    -H "Content-Type: application/json"  \
+    -d '{
+        "model": "baichuan-m3",
+        "messages": [
+            {
+                "role": "user",
+                "content": "下午头痛怎么办？"
+            }
+        ]
+}'
+```
+
+## 效果展示
+<div align=center>
+    <img src="./doc/result.png"/>
+</div>
+
+### transformer
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+model_path = "/path/to/baichuan-inc/Baichuan-M3-235B"
+import os
+import torch
+os.environ['TRANSFORMERS_OFFLINE'] = '1'
+os.environ['MODELSCOPE_OFFLINE'] = '1'
+model = AutoModelForCausalLM.from_pretrained(
+  model_path, 
+  trust_remote_code=True,
+  device_map="auto",
+  torch_dtype=torch.bfloat16
+  )
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+messages = [{"role": "user", "content": "I've been having headaches lately, especially worse in the afternoon. What should I do?"}]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    thinking_mode='on'
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32768,
+    temperature=0.6
+)
+response = tokenizer.decode(generated_ids[0][len(model_inputs.input_ids[0]):], skip_special_tokens=True)
+print(response)
+```
+
+
+### 精度
+`DCU与GPU精度一致，推理框架：vllm。`
+
+## 预训练权重
+| 模型名称  | 权重大小  | DCU型号  | 最低卡数需求 |下载地址|
+|:-----:|:----------:|:----------:|:---------------------:|:----------:|
+| Baichuan-M3-235B | 235B | BW1000  | 8  | [ModelScope](https://modelscope.cn/models/baichuan-inc/Baichuan-M3-235B) |
+
+## 参考资料
+- https://www.baichuan-ai.com/blog/baichuan-M3
+

model.properties

+# 模型唯一标识
+modelCode=Project ID(GitLab创建项目后查看名称下面的Project ID即可，注意此ID为hpccube下GitLab生成的ID，不可编造。例如:365)
+# 模型名称
+modelName=模型名称(同项目名称,模型名_深度学习框架：模型名为源github项目名(官方名称)，深度学习框架名采用小写格式。例如:Janus_pytorch,resnet50_tensorflow)
+# 模型描述
+modelDescription=简要描述此模型(尽量50字以内)
+# 运行过程
+processType=推理,训练(标签用英文逗号隔开。)
+# 算法类别
+appCategory=OCR(与icon图标名称一致，请勿随意命名, 全部类别请查看：https://r0ddbu55vzx.feishu.cn/drive/folder/AgoUfBk5IlYTV1dBz2YcGSYUnDf)
+# 框架类型
+frameType=paddle(说明使用的算法框架, 多个标签用英文逗号隔开。)
+# 加速卡类型
+accelerateType=BW1000,K100AI(设备为项目中所运行模型测试所用的加速卡，以帮助用户在光源可快速进行目标选型。)