Text Generation
Transformers
Safetensors
English
llama
medical
clinical-reasoning
chain-of-thought
sft
conversational
text-generation-inference
Instructions to use BrainHealthAI/BrainMed-8B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| # Paired significance: `brainmed-8b-v1__soup-last-a0.3` vs `base-HuatuoGPT-o1-8B` | |
| McNemar's exact test on the items both runs answered. `only B` counts items the | |
| candidate gets right and the baseline gets wrong; `only A` the reverse. Items both | |
| get right or both get wrong are uninformative about which model is better. | |
| | Benchmark | n | base-HuatuoGPT-o1-8B | brainmed-8b-v1__soup-last-a0.3 | Delta | only A | only B | p | verdict (a=0.05) | | |
| |---|---|---|---|---|---|---|---|---| | |
| | MB-op4 | 298 | 59.73 | 62.42 | +2.68 | 30 | 38 | 0.396 | no difference | | |
| | MB-op5 | 298 | 55.37 | 53.36 | -2.01 | 37 | 31 | 0.545 | no difference | | |
| | MedMCQA | 4183 | 63.28 | 64.04 | +0.77 | 358 | 390 | 0.257 | no difference | | |
| | MedQA | 1273 | 77.93 | 76.59 | -1.34 | 103 | 86 | 0.244 | no difference | | |
| | MedXpert | 1449 | 16.98 | 18.63 | +1.66 | 93 | 117 | 0.112 | no difference | | |
| | PubMedQA | 1000 | 80.2 | 79.1 | -1.10 | 30 | 19 | 0.152 | no difference | | |
| ## Pooled across benchmarks | |
| - items only `base-HuatuoGPT-o1-8B` gets right: **651** | |
| - items only `brainmed-8b-v1__soup-last-a0.3` gets right: **681** | |
| - McNemar exact p = **0.4269** | |
| Pooled, the two runs are **statistically indistinguishable** (p = 0.427). Any average difference between them is within sampling noise. | |
| Per benchmark: 0 significantly better, 0 significantly worse, 6 indistinguishable. | |
| Pooling treats benchmarks as one sample and so weights them by size; read it | |
| alongside the per-benchmark rows, not instead of them. | |