Instructions to use BrainHealthAI/BrainMed-8B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BrainHealthAI/BrainMed-8B-SFT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BrainHealthAI/BrainMed-8B-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BrainHealthAI/BrainMed-8B-SFT")
model = AutoModelForCausalLM.from_pretrained("BrainHealthAI/BrainMed-8B-SFT", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BrainHealthAI/BrainMed-8B-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BrainHealthAI/BrainMed-8B-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BrainHealthAI/BrainMed-8B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BrainHealthAI/BrainMed-8B-SFT

SGLang

How to use BrainHealthAI/BrainMed-8B-SFT with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BrainHealthAI/BrainMed-8B-SFT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BrainHealthAI/BrainMed-8B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BrainHealthAI/BrainMed-8B-SFT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BrainHealthAI/BrainMed-8B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BrainHealthAI/BrainMed-8B-SFT with Docker Model Runner:
```
docker model run hf.co/BrainHealthAI/BrainMed-8B-SFT
```

BrainMed-8B-SFT

File size: 3,244 Bytes

64e62d7

---
license: apache-2.0
base_model: FreedomIntelligence/HuatuoGPT-o1-8B
library_name: transformers
pipeline_tag: text-generation
tags:
- medical
- clinical-reasoning
- chain-of-thought
- sft
language:
- en
---

# BrainMed-8B-SFT

Full-parameter supervised fine-tune of `FreedomIntelligence/HuatuoGPT-o1-8B` on
[`Williamsanderson/MedReason-MedO1-Reasoning-46K`](https://huggingface.co/datasets/Williamsanderson/MedReason-MedO1-Reasoning-46K), a union of
KG-grounded MedReason reasoning traces and verifier-checked medical-o1 traces.

- **Parameters:** 8,030,261,248 (8.03B) - full weights, not an adapter
- **Precision:** BF16
- **Recipe:** MedReason (arXiv:2504.00993) - lr 5e-6, effective batch 128, 3 epochs,
  cosine schedule with 5% warmup, weight decay 0.1, DeepSpeed ZeRO-3, bf16
- **Weights obtained by:** lowest held-out validation loss (val loss 0.7557 at optimizer step 1041)

## Output format

The model is trained to reason inside `<think>...</think>` and answer inside
`<answer>...</answer>`, under this system prompt:

```
You are a medical reasoning assistant. Work through the clinical problem step by step inside <think>...</think>, grounding every step in established medical knowledge, then give the final, complete answer inside <answer>...</answer>.
```

## Evaluation

| Benchmark | n | Accuracy |
|---|---|---|
| medbullets_op4 | 308 | 58.77 |
| medbullets_op5 | 308 | 56.82 |
| medmcqa_val | 4183 | 61.2 |
| medqa_4opt | 1273 | 72.11 |
| medxpertqa | 1449 | 18.08 |
| pubmedqa_test | 1000 | 77.1 |



Scored with the MedReason evaluation harness (greedy decoding, strict answer prompt,
`max(head, tail)` extraction) on the benchmark files shipped by the MedReason and
HuatuoGPT-o1 repositories - no benchmark was rebuilt or resampled.

## Data integrity

Training rows: **44,351**.

Rows overlapping the evaluation benchmarks were **kept**, matching the upstream setting under which the published comparison tables were produced. The overlap was measured and is declared below rather than removed; the evaluation therefore reports a clean-subset score alongside the raw one, and the clean figure is the unbiased estimate.

| Benchmark | items | overlapping training rows |
|---|---|---|
| hle_med | 103 | 30 (29.13%) |
| medbullets_op4 | 308 | 35 (11.36%) |
| medbullets_op5 | 308 | 35 (11.36%) |
| medqa_4opt | 1273 | 1 (0.08%) |
| medqa_5opt | 1273 | 1 (0.08%) |
| medxpertqa | 1449 | 15 (1.04%) |
| mmlu_pro_medical | 1535 | 340 (22.15%) |

This overlap originates upstream: the MedReason corpus derives reasoning traces from MMLU-medical, MedXpertQA and Humanity's Last Exam, three sets it also evaluates on.

Answer-format alignment was applied to 17,083 rows (38.52%): a canonical `The answer is X.` was appended where the letter was unambiguously recoverable from options already present in the question. Questions and reasoning traces are byte-identical to the source.


## Intended use

Research and decision support. Not a medical device; not for autonomous clinical use.

## Citation

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