Instructions to use Intelligent-Internet/II-Medical-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Intelligent-Internet/II-Medical-8B with Transformers:

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

pipe = pipeline("text-generation", model="Intelligent-Internet/II-Medical-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Intelligent-Internet/II-Medical-8B")
model = AutoModelForCausalLM.from_pretrained("Intelligent-Internet/II-Medical-8B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Intelligent-Internet/II-Medical-8B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Intelligent-Internet/II-Medical-8B

SGLang

How to use Intelligent-Internet/II-Medical-8B 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 "Intelligent-Internet/II-Medical-8B" \
    --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": "Intelligent-Internet/II-Medical-8B",
		"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 "Intelligent-Internet/II-Medical-8B" \
        --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": "Intelligent-Internet/II-Medical-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Intelligent-Internet/II-Medical-8B with Docker Model Runner:
```
docker model run hf.co/Intelligent-Internet/II-Medical-8B
```

hoanganhpham commited on May 15, 2025

Commit

d8f6f97

1 Parent(s): 9295141

Update README.md

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README.md +4 -1

README.md CHANGED Viewed

@@ -15,6 +15,9 @@ II-Medical-8B is a medical reasoning model trained on a [comprehensive dataset](
 ![Model Benchmark](https://cdn-uploads.huggingface.co/production/uploads/6389496ff7d3b0df092095ed/uvporIhY4_WN5cGaGF1Cm.png)
 ## II. Training Methodology
 We collected and generated a comprehensive set of reasoning datasets for the medical domain and performed SFT fine-tuning on the **Qwen/Qwen3-8B** model. Following this, we further optimized the SFT model by training DAPO on a hard-reasoning dataset to boost performance.
@@ -47,7 +50,7 @@ Journal of Medicine,  4 Options  and 5 Options splits from the MedBullets platfo
 | Model                   | MedMC | MedQA | PubMed | MMLU-P | GPQA | Lancet | MedB-4 | MedB-5 | MedX  | NEJM  | Avg   |
 |--------------------------|-------|-------|--------|--------|------|--------|--------|--------|------|-------|-------|
-| QWQ 32B                  | 76.76 | 88.85 | 79.90   | 80.46  | 64.36| 70.87   | 77.27  | 73.05  |23.53 |76.29  | 71.13 |
 | QWQ 32B                  | 69.73 | 87.03 | 88.5   | 79.86  | 69.17| 71.3   | 72.07  | 69.01  |24.98 |75.12  | 70.68 |
 | Qwen2.5-7B-IT            | 56.56 | 61.51 | 71.3   | 61.17  | 42.56| 61.17  | 46.75  | 40.58  |13.26 |59.04  | 51.39 |
 | HuatuoGPT-o1-8B          | 63.97 | 74.78 | **80.10**  | 63.71  | 55.38| 64.32  | 58.44  | 51.95  |15.79 |64.84  | 59.32 |

 ![Model Benchmark](https://cdn-uploads.huggingface.co/production/uploads/6389496ff7d3b0df092095ed/uvporIhY4_WN5cGaGF1Cm.png)
+Our II-Medical-8B model achieved a 40% score on HealthBench, an open-source benchmark evaluating the performance and safety of large language models in healthcare, performing comparably to OpenAI's o1 reasoning model
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6389496ff7d3b0df092095ed/S90HEqD6UJCme-1_17IJw.png)
 ## II. Training Methodology
 We collected and generated a comprehensive set of reasoning datasets for the medical domain and performed SFT fine-tuning on the **Qwen/Qwen3-8B** model. Following this, we further optimized the SFT model by training DAPO on a hard-reasoning dataset to boost performance.
 | Model                   | MedMC | MedQA | PubMed | MMLU-P | GPQA | Lancet | MedB-4 | MedB-5 | MedX  | NEJM  | Avg   |
 |--------------------------|-------|-------|--------|--------|------|--------|--------|--------|------|-------|-------|
+| HuatuoGPT-o1-72B         | 76.76 | 88.85 | 79.90   | 80.46  | 64.36| 70.87   | 77.27  | 73.05  |23.53 |76.29  | 71.13 |
 | QWQ 32B                  | 69.73 | 87.03 | 88.5   | 79.86  | 69.17| 71.3   | 72.07  | 69.01  |24.98 |75.12  | 70.68 |
 | Qwen2.5-7B-IT            | 56.56 | 61.51 | 71.3   | 61.17  | 42.56| 61.17  | 46.75  | 40.58  |13.26 |59.04  | 51.39 |
 | HuatuoGPT-o1-8B          | 63.97 | 74.78 | **80.10**  | 63.71  | 55.38| 64.32  | 58.44  | 51.95  |15.79 |64.84  | 59.32 |