README.md

---
license: apache-2.0
base_model: FreedomIntelligence/HuatuoGPT-o1-8B
datasets:
- BrainHealthAI/MedCortex-v1
language:
- en
tags:
- medical
- clinical-reasoning
- chain-of-thought
- full-fine-tune
pipeline_tag: text-generation
---

# MedCortex-Huatuo-8B-e1

Full fine-tune of **FreedomIntelligence/HuatuoGPT-o1-8B** on the **reasoning** subset of
**BrainHealthAI/MedCortex-v1** (English, verified chain-of-thought, Apache-2.0).

This reproduces the MedReason recipe (arXiv:2504.00993) with the **only variable being the training
data**: same base, same official `SFT.py` recipe, same official evaluation harness. It is an
internal research checkpoint for a controlled post-training evaluation.

## Training
- **Base:** FreedomIntelligence/HuatuoGPT-o1-8B (itself Llama-3.1-8B + medical RL)
- **Data:** MedCortex-v1 `task_type=reasoning`, English (42086 examples)
- **Recipe:** full FT (no LoRA), 1 epoch, LR 5e-6, cosine, warmup 0.05, global batch ~128,
  bf16, DeepSpeed ZeRO-3, seq_len 4096, seed 2002
- **Response format:** `## Thinking\n\n<reasoning>\n\n## Final Answer\n\n<answer>`

## Prompt format
Use the Llama-3.1 chat template; ask a clinical/exam question and the model reasons under
`## Thinking` then answers under `## Final Answer`.

## Evaluation
Evaluated with the **official MedReason harness** (SGLang + `eval.py` + `scorer.py`, strict prompt),
so scores are drop-in comparable to MedReason paper Table 4.



## Intended use & limitations
Decision support / research only — **not a medical device**; every output requires a licensed
professional. 1-epoch checkpoint (paper uses 3): an early read on data quality, not a final model.

## Citation
```bibtex
@misc{wu2025medreason, title={MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs},
  author={Juncheng Wu and others}, year={2025}, eprint={2504.00993}, archivePrefix={arXiv}}
```