Instructions to use vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical with Transformers:

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

pipe = pipeline("text-generation", model="vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical")
model = AutoModelForCausalLM.from_pretrained("vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical")
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]:]))

llama-cpp-python

How to use vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical",
	filename="OpenR1-Distill-Qwen3-8B-Medical-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16
# Run inference directly in the terminal:
llama cli -hf vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16
# Run inference directly in the terminal:
llama cli -hf vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16
# Run inference directly in the terminal:
./llama-cli -hf vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16

Use Docker

docker model run hf.co/vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16

LM Studio
Jan

vLLM

How to use vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16

SGLang

How to use vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical 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 "vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical" \
    --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": "vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical",
		"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 "vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical" \
        --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": "vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical with Ollama:
```
ollama run hf.co/vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16
```

Unsloth Studio

How to use vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical to start chatting

How to use vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical with Docker Model Runner:
```
docker model run hf.co/vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16
```

Lemonade

How to use vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical:F16

Run and chat with the model

lemonade run user.OpenR1-Distill-Qwen3-8B-Medical-F16

List all available models

lemonade list

Model Card for OpenR1-Distill-Qwen3-8B-Medical

This model is a fine-tuned version of Qwen/Qwen3-8B on two merged datasets:

FreedomIntelligence/medical-o1-reasoning-SFT (https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT) Intelligent-Internet/II-Medical-Reasoning-SFT (https://huggingface.co/datasets/Intelligent-Internet/II-Medical-Reasoning-SFT)

It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.23.0
Transformers: 4.53.0
Pytorch: 2.6.0
Datasets: 4.3.0
Tokenizers: 0.21.4

Citations

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}

Downloads last month: 13

Safetensors

Model size

308k params

Tensor type

BF16

Model tree for vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Quantized

(344)

this model

vinhnguyenxu
/

OpenR1-Distill-Qwen3-8B-Medical

Model Card for OpenR1-Distill-Qwen3-8B-Medical

Quick start

Training procedure

Framework versions

Citations

Model tree for vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical

Dataset used to train vinhnguyenxu/OpenR1-Distill-Qwen3-8B-Medical