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README.md 0 → 100644
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# 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 0 → 100644
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# 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。)
vocab.json 0 → 100644
View file @ edcd2fb1
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