Instructions to use vadimbelsky/qwen3.5-medical-sft-stage2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vadimbelsky/qwen3.5-medical-sft-stage2 with Transformers:

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

pipe = pipeline("text-generation", model="vadimbelsky/qwen3.5-medical-sft-stage2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("vadimbelsky/qwen3.5-medical-sft-stage2")
model = AutoModelForImageTextToText.from_pretrained("vadimbelsky/qwen3.5-medical-sft-stage2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vadimbelsky/qwen3.5-medical-sft-stage2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vadimbelsky/qwen3.5-medical-sft-stage2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vadimbelsky/qwen3.5-medical-sft-stage2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vadimbelsky/qwen3.5-medical-sft-stage2

SGLang

How to use vadimbelsky/qwen3.5-medical-sft-stage2 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 "vadimbelsky/qwen3.5-medical-sft-stage2" \
    --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": "vadimbelsky/qwen3.5-medical-sft-stage2",
		"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 "vadimbelsky/qwen3.5-medical-sft-stage2" \
        --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": "vadimbelsky/qwen3.5-medical-sft-stage2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use vadimbelsky/qwen3.5-medical-sft-stage2 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 vadimbelsky/qwen3.5-medical-sft-stage2 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 vadimbelsky/qwen3.5-medical-sft-stage2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vadimbelsky/qwen3.5-medical-sft-stage2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="vadimbelsky/qwen3.5-medical-sft-stage2",
    max_seq_length=2048,
)

Docker Model Runner
How to use vadimbelsky/qwen3.5-medical-sft-stage2 with Docker Model Runner:
```
docker model run hf.co/vadimbelsky/qwen3.5-medical-sft-stage2
```

Qwen3.5-9B Medical Triage — Stage 2 (Merged)

Merged full-precision (bfloat16) model. Fine-tuned in two stages on clinical triage data using Unsloth.

Training

	Stage 1	Stage 2
Base	Qwen/Qwen2.5-7B	merged Stage 1
Dataset	~50K synthetic PubMed QA pairs	~9.2K clinical intake notes (SOAP format, ESI 1-5)
LoRA rank	r=16, α=16	r=32, α=64
Learning rate	2e-4	1e-4
Max seq length	4 096	4 096
Epochs	1	1
Batch size	8 (eff. 16)	8 (eff. 16)
Precision	bf16	bf16

Stage 1 LoRA adapters were merged into base weights before Stage 2 to give a stable optimizer foundation.

Dataset — Stage 2

— 9 238 synthetic SOAP-format clinical intake notes with:

Chief complaint
ESI triage level (1–5)
Full SOAP note
Triage decision rationale

System Prompt

Usage

Demo

Medical Triage Assistant — Hugging Face Space

Limitations & Disclaimer

This model is for research and educational purposes only. It must not be used for clinical decision-making or as a substitute for professional medical judgment. Outputs may be incorrect, incomplete, or misleading. Always consult a qualified clinician.

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Safetensors

Model size

10B params

Tensor type

BF16

F32

Model tree for vadimbelsky/qwen3.5-medical-sft-stage2

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Qwen/Qwen2.5-7B

Finetuned

(961)

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