Dragnoz/Medical-Reasoning-SFT-Mega

---
license: apache-2.0
task_categories:
- text-generation
- question-answering
language:
- en
tags:
- medical
- reasoning
- healthcare
- clinical
- chain-of-thought
- thinking
- sft
- mega
- combined
size_categories:
- 1M<n<10M
---

# Medical-Reasoning-SFT-Mega

The ultimate medical reasoning dataset - combining 7 state-of-the-art AI models with fair distribution deduplication. 1.79 million unique samples with 3.78 billion tokens of medical chain-of-thought reasoning.

## Dataset Overview

| Metric | Value |
|--------|-------|
| **Total Samples** | 1,789,998 (after deduplication) |
| **Total Tokens** | ~3.78 Billion |
| **Content Tokens** | ~2.22 Billion |
| **Reasoning Tokens** | ~1.56 Billion |
| **Samples with Reasoning** | 1,789,764 (100.0%) |
| **Unique Questions** | 1,237,711 |
| **Shared Questions** | 552,287 |
| **Source Models** | 7 |
| **Language** | English |

## Source Models

This dataset combines reasoning from 7 leading AI models with fair representation:

| Model | Original | Unique | From Shared | Final | % |
|-------|----------|--------|-------------|-------|---|
| MiniMax-M2.1 | 204,773 | 76,892 | 42,126 | 119,018 | 6.6% |
| Baichuan-M3-235B | 124,520 | 30,589 | 43,070 | 73,659 | 4.1% |
| GPT-OSS-120B | 506,150 | 403,208 | 42,113 | 445,321 | 24.9% |
| Qwen3-Next-80B | 604,249 | 295,828 | 118,539 | 414,367 | 23.1% |
| GLM_4.5_Air | 225,179 | 151,278 | 42,117 | 193,395 | 10.8% |
| Nemotron-Nano-30B | 444,544 | 86,978 | 118,546 | 205,524 | 11.5% |
| Trinity-Mini | 810,284 | 192,938 | 145,776 | 338,714 | 18.9% |

**Total before deduplication:** 2,919,699 samples
**Total after deduplication:** 1,789,998 samples

## Deduplication Strategy: Fair Distribution

When the same medical question appears in multiple source datasets, we use **fair distribution** to ensure balanced representation:

1. **Unique questions** (1,237,711): Questions appearing in only one model are included directly
2. **Shared questions** (552,287): Questions appearing in multiple models are distributed fairly across all contributing models

For shared questions, we assign each question to the model that has been assigned the fewest shared questions so far. This ensures no single model dominates the duplicate pool, resulting in diverse reasoning styles across the dataset.

This differs from priority-based deduplication where one model would "win" all duplicates. With fair distribution, each model gets roughly equal share of the shared question pool.

## 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..."
}
]
}
```

### 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-Mega")
```

### 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 with diverse reasoning styles from multiple state-of-the-art models
- **Chain-of-thought training**: Develop models that show detailed thinking processes
- **Medical QA systems**: Build robust question-answering systems for healthcare applications
- **Research**: Study and compare reasoning patterns across different AI architectures
- **Distillation**: Transfer capabilities from multiple large models to smaller ones

## Why Mega?

1. **Diversity**: 7 different model architectures provide varied reasoning approaches
2. **Fair Representation**: Fair distribution ensures balanced contributions from all models
3. **Scale**: 1.79M unique samples and 3.78B tokens for comprehensive training
4. **Coverage**: Spans clinical, diagnostic, pharmacological, and general medical knowledge

## Limitations and Considerations

- This dataset is generated by AI models 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 model approaches, not necessarily optimal clinical reasoning

## Related Datasets

Individual model datasets are also available:

- [Medical-Reasoning-SFT-MiniMax-M2.1](https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-MiniMax-M2.1)
- [Medical-Reasoning-SFT-Baichuan-M3-235B](https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Baichuan-M3-235B)
- [Medical-Reasoning-SFT-GPT-OSS-120B-V2](https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-GPT-OSS-120B-V2)
- [Medical-Reasoning-SFT-Qwen3-Next-80B](https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Qwen3-Next-80B)
- [Medical-Reasoning-SFT-GLM_4.5_Air](https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-GLM_4.5_Air)
- [Medical-Reasoning-SFT-Nemotron-Nano-30B](https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Nemotron-Nano-30B)
- [Medical-Reasoning-SFT-Trinity-Mini](https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Trinity-Mini)

## Citation

If you use this dataset, please cite:

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

## License

Apache 2.0