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 / evaluation /_card /README.md

Williamsanderson

evaluation results and figures

8a33135 verified 17 days ago

preview code

Raw

History Blame Contribute Delete

5.51 kB

metadata

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

Full-parameter fine-tune of FreedomIntelligence/HuatuoGPT-o1-8B on Williamsanderson/MedReason-MedO1-Reasoning-46K — a union of KG-grounded MedReason reasoning traces and verifier-checked medical-o1 traces.

Research model. Not medical advice, not a medical device, not clinically validated. Do not use it for decisions about any real person.

Results

Accuracy (%) on the six benchmarks of MedReason Table 4. The BrainMed-8B row was measured here; every other row is transcribed from the MedReason paper (arXiv:2504.00993) and was not re-run.

Model	MedBullets op4	MedBullets op5	MedXpertQA	MedQA	MedMCQA	PubMedQA	Avg	Source
Medical-Llama3-8B	33.4	25.3	9.0	40.3	46.8	48.0	33.8	paper
Mistral-Instruct-7B	43.5	33.4	11.4	48.2	44.9	50.1	38.6	paper
BioMistral-7B	46.4	33.1	12.4	45.0	40.2	66.9	40.7	paper
Medical-CoT-8B	39.3	34.1	12.6	49.0	42.6	68.0	40.9	paper
DeepSeek-Distill-8B	41.9	35.1	13.5	55.4	49.0	73.9	44.8	paper
OpenBioLLM-8B	39.2	35.7	10.7	57.7	54.1	74.1	45.3	paper
Llama3.1-Instruct-8B	43.2	40.9	14.3	58.7	56.0	75.2	48.0	paper
Qwen2.5-Instruct-7B	50.0	41.6	12.6	57.0	55.6	72.7	48.2	paper
Huatuo-o1-SFT-8B	53.3	49.7	17.3	70.2	58.2	76.1	54.1	paper
Huatuo-o1-RL-8B	55.2	51.3	16.7	72.6	60.4	79.2	55.9	paper
MedReason-8B	57.5	55.5	19.0	71.8	60.7	79.4	57.3	paper
BrainMed-8B	62.01	54.87	18.63	76.67	64.07	79.10	59.23	measured

Additional benchmarks

Benchmark	n	BrainMed-8B
MMLU-Pro (Med)	1535	65.73
HLE (med)	103	10.68
GPQA (Med)	390	59.74
MedQA (5-opt)	1273	73.68

Read the comparison with this caveat. Our numbers come from our own evaluation harness (greedy decoding, the training system prompt applied at inference). Scoring the untouched backbone on that harness gives +2.89 points over its published row, so part of the gap to the paper rows is protocol, not model. The comparison that is free of this effect is the before/after against the backbone measured on the same harness, published in evaluation/REPORT.md.

Sample answers

Produced by this checkpoint, greedy decoding, with the system prompt below.

Multiple choice, strict format

Differential diagnosis

Note what these show and what they do not: the model is trained and evaluated on medical question answering. It handles multiple choice and differential diagnosis well. It is weaker on open-ended acute management — its corpus contains QA pairs, not treatment protocols — and it should not be relied on there.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "BrainHealthAI/BrainMed-8B"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

SYSTEM = ("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>.")

messages = [{"role": "system", "content": SYSTEM},
            {"role": "user", "content": "How is eclampsia-related seizure managed?"}]
inputs = tok(tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True),
             return_tensors="pt").to(model.device)
print(tok.decode(model.generate(**inputs, max_new_tokens=1024)[0], skip_special_tokens=True))

The system prompt is part of the contract: the model was trained under it and answers as <think>…</think><answer>…</answer>. Dropping it measurably lowers accuracy.

Training

MedReason recipe (arXiv:2504.00993): lr 5e-6, effective batch 128, 3 epochs, cosine schedule with 5% warmup, weight decay 0.1, DeepSpeed ZeRO-3, bf16, full parameters — no adapter. 4×H100, ~1.5 h. Full pipeline, evaluation logs and figures are under evaluation/.

Citation

@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}
}