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

Libraries

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

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("suayptalha/Qwen3-0.6B-Treatment")
model = AutoModelForCausalLM.from_pretrained("suayptalha/Qwen3-0.6B-Treatment", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

Qwen3-0.6B-Treatment-Expert

This project performs full fine-tuning on the Qwen3-0.6B language model to enhance its clinical treatment planning and reasoning capabilities. The model was optimized using the bfloat16 (bf16) data type.

Training Procedure

Dataset Preparation
- Dataset: Containing paired clinical diagnosis descriptions and corresponding step-by-step treatment plans.
Model Loading and Configuration
- Base model: Qwen3-0.6B, loaded with the unsloth library in bf16 precision.
- Full fine-tuning (full_finetuning=True) applied to all layers to adapt the model for medical treatment tasks.
Supervised Fine-Tuning (SFT)
- Utilized the Hugging Face TRL library with the Supervised Fine-Tuning approach.
- The model was trained to generate both intermediate reasoning steps and final treatment recommendations.
- Training hyperparameters:
  - Epochs: 2
  - Learning rate: 2e-5
  - Batch size: 8

Purpose and Outcome

Significantly improved the model’s ability to interpret clinical diagnoses and propose structured treatment plans.

Evaluation

Performance was measured on a held-out validation set with the following metrics:
- Plan Fidelity: 59.69% similarity with DeepSeek V3-0324.
- Reasoning Coherence: Rated high by a panel of medical experts.