---
title: Medical-Reasoning-SFT-Baichuan-M3-235B
canonical_url: "https://www.modelscope.cn/datasets/OpenMed/Medical-Reasoning-SFT-Baichuan-M3-235B"
md_url: "https://www.modelscope.cn/datasets/OpenMed/Medical-Reasoning-SFT-Baichuan-M3-235B.md"
repository: OpenMed/Medical-Reasoning-SFT-Baichuan-M3-235B
last_updated: 2026-02-05
license: apache-2.0
storage_size: "638 MB"
downloads: 87
stars: 2
---

# Medical-Reasoning-SFT-Baichuan-M3-235B

> Medical-Reasoning-SFT-Baichuan-M3-235B - OpenMed 在 ModelScope 开源的数据集。Medical-Reasoning-SFT-Baichuan-M3-235B

OpenMed/Medical-Reasoning-SFT-Baichuan-M3-235B 是 ModelScope 魔搭社区上的数据集，存储大小 638 MB，采用 apache-2.0 许可。

- **Repository**: OpenMed/Medical-Reasoning-SFT-Baichuan-M3-235B
- **License**: apache-2.0
- **Storage size**: 638 MB
- **Downloads**: 87
- **Stars**: 2
- **Last updated**: 2026-02-05

Source: https://www.modelscope.cn/datasets/OpenMed/Medical-Reasoning-SFT-Baichuan-M3-235B

---

# Medical-Reasoning-SFT-Baichuan-M3-235B

A large-scale medical reasoning dataset generated using [baichuan-inc/Baichuan-M3-235B](https://huggingface.co/baichuan-inc/Baichuan-M3-235B), containing over 124,000 samples with detailed chain-of-thought reasoning for medical and healthcare questions.

**Baichuan-M3-235B is ranked #1 on HealthBench Total leaderboard and achieves state-of-the-art performance on medical reasoning benchmarks.**

## Dataset Overview

| Metric | Value |
|--------|-------|
| **Model** | baichuan-inc/Baichuan-M3-235B |
| **Total Samples** | 124,520 |
| **Samples with Reasoning** | 124,520 (100%) |
| **Estimated Tokens** | ~255 Million |
| **Content Tokens** | ~160 Million |
| **Reasoning Tokens** | ~95 Million |
| **Language** | English |

## Why Baichuan-M3-235B?

Baichuan-M3-235B is a purpose-built medical AI model with exceptional health evaluation results:

### HealthBench Performance
- **#1 on HealthBench Total Leaderboard** - Top-ranked model globally
- **HealthBench-Hard: 44.4%** - A 28-point improvement over M2, surpassing GPT-5.2
- **Industry-lowest hallucination rate: 3.5%** - Achieved through innovative Fact-Aware RL training

### Clinical Benchmarks
- **SCAN-Bench: First Place** - Across all three dimensions:
  - Clinical Inquiry
  - Lab Testing
  - Final Diagnosis
- **SPAR Algorithm** - Segmented Pipeline Reinforcement Learning specifically designed for clinical decision-making

### Model Architecture
- **Parameters**: 235B
- **Base**: Qwen3-235B-A22B
- **License**: Apache 2.0

## Schema

Each sample follows the conversational messages format with reasoning content:

```json
{
  "messages": [
    {
      "role": "system",
      "content": "You are a medical expert...",
      "reasoning_content": null
    },
    {
      "role": "user",
      "content": "What are the symptoms of diabetes?",
      "reasoning_content": null
    },
    {
      "role": "assistant",
      "content": "The main symptoms of diabetes include...",
      "reasoning_content": "Let me think through this systematically. Diabetes affects blood sugar regulation, so I should consider symptoms related to hyperglycemia..."
    }
  ]
}
```

### Fields

| Field | Type | Description |
|-------|------|-------------|
| `messages` | list | Array of message objects in the conversation |
| `messages[].role` | string | Either "system", "user", or "assistant" |
| `messages[].content` | string | The main message content |
| `messages[].reasoning_content` | string or null | Chain-of-thought reasoning (assistant messages only) |

## Usage

### Loading with Datasets Library

```python
from datasets import load_dataset

dataset = load_dataset("OpenMed/Medical-Reasoning-SFT-Baichuan-M3-235B")
```

### Accessing Samples

```python
# Get a sample
sample = dataset['train'][0]

# Access messages
for msg in sample['messages']:
    print(f"Role: {msg['role']}")
    print(f"Content: {msg['content'][:100]}...")
    if msg['reasoning_content']:
        print(f"Reasoning: {msg['reasoning_content'][:100]}...")
```

### Filtering by Reasoning

```python
# Get samples with reasoning content
samples_with_reasoning = dataset['train'].filter(
    lambda x: x['messages'][-1]['reasoning_content'] is not None
)
```

## Intended Use

This dataset is designed for:

- **Fine-tuning medical reasoning models**: Train LLMs to provide detailed, step-by-step medical reasoning
- **Chain-of-thought training**: Develop models that show their thinking process
- **Medical QA systems**: Build question-answering systems for healthcare applications
- **Research**: Study reasoning patterns in medical domain AI

## Limitations and Considerations

- This dataset is generated by an AI model and should not be used as a substitute for professional medical advice
- Responses may contain inaccuracies and should be validated by medical professionals
- Not intended for clinical decision-making without expert review
- The reasoning traces reflect the model's approach, not necessarily optimal clinical reasoning

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{medical_reasoning_sft_baichuan_m3_235b,
  title={Medical-Reasoning-SFT-Baichuan-M3-235B},
  author={OpenMed},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Baichuan-M3-235B}
}
```

## License

Apache 2.0