Instructions to use suayptalha/Qwen3-0.6B-Medical-Expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use suayptalha/Qwen3-0.6B-Medical-Expert with Transformers:

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

pipe = pipeline("text-generation", model="suayptalha/Qwen3-0.6B-Medical-Expert")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("suayptalha/Qwen3-0.6B-Medical-Expert")
model = AutoModelForCausalLM.from_pretrained("suayptalha/Qwen3-0.6B-Medical-Expert", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use suayptalha/Qwen3-0.6B-Medical-Expert with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "suayptalha/Qwen3-0.6B-Medical-Expert"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suayptalha/Qwen3-0.6B-Medical-Expert",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/suayptalha/Qwen3-0.6B-Medical-Expert

SGLang

How to use suayptalha/Qwen3-0.6B-Medical-Expert 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 "suayptalha/Qwen3-0.6B-Medical-Expert" \
    --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": "suayptalha/Qwen3-0.6B-Medical-Expert",
		"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 "suayptalha/Qwen3-0.6B-Medical-Expert" \
        --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": "suayptalha/Qwen3-0.6B-Medical-Expert",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use suayptalha/Qwen3-0.6B-Medical-Expert 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 suayptalha/Qwen3-0.6B-Medical-Expert 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 suayptalha/Qwen3-0.6B-Medical-Expert to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for suayptalha/Qwen3-0.6B-Medical-Expert to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="suayptalha/Qwen3-0.6B-Medical-Expert",
    max_seq_length=2048,
)

Docker Model Runner
How to use suayptalha/Qwen3-0.6B-Medical-Expert with Docker Model Runner:
```
docker model run hf.co/suayptalha/Qwen3-0.6B-Medical-Expert
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-0.6B-Medical-Expert

This project performs full fine-tuning on the Qwen3-0.6B language model to enhance its medical reasoning and clinical understanding capabilities. Training was conducted on the FreedomIntelligence/medical-o1-reasoning-SFT dataset using bfloat16 (bf16) precision for efficient optimization.

Training Procedure

Dataset Preparation
- The FreedomIntelligence/medical-o1-reasoning-SFT dataset was used.
- Each example consists of medically relevant instructions or questions paired with detailed, step-by-step clinical reasoning responses.
- Prompts were structured to encourage safe, factual, and coherent medical reasoning chains.
Model Loading and Configuration
- Qwen3 base model weights were loaded via the unsloth library in bf16 precision.
- All model layers were fully updated (full_finetuning=True) to effectively adapt the model to medical reasoning and decision-making tasks.
Supervised Fine-Tuning
- Fine-tuning was conducted using the Hugging Face TRL library with the Supervised Fine-Tuning (SFT) approach.
- The model was trained to follow clinical instructions, interpret symptoms, and generate reasoned diagnoses or treatment suggestions.

Purpose and Outcome

The model’s ability to interpret medical instructions and generate step-by-step clinical reasoning has been significantly enhanced.
It produces responses that combine factual accuracy with transparent reasoning, making it useful in educational and assistive medical AI contexts.