Instructions to use vadimbelsky/qwen3.5-medical-ft-stage3-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vadimbelsky/qwen3.5-medical-ft-stage3-dpo with Transformers:

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

pipe = pipeline("image-text-to-text", model="vadimbelsky/qwen3.5-medical-ft-stage3-dpo")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("vadimbelsky/qwen3.5-medical-ft-stage3-dpo")
model = AutoModelForMultimodalLM.from_pretrained("vadimbelsky/qwen3.5-medical-ft-stage3-dpo", device_map="auto")
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 Settings

vLLM

How to use vadimbelsky/qwen3.5-medical-ft-stage3-dpo 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-ft-stage3-dpo"
# 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-ft-stage3-dpo",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/vadimbelsky/qwen3.5-medical-ft-stage3-dpo

SGLang

How to use vadimbelsky/qwen3.5-medical-ft-stage3-dpo 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-ft-stage3-dpo" \
    --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-ft-stage3-dpo",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-ft-stage3-dpo" \
        --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-ft-stage3-dpo",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use vadimbelsky/qwen3.5-medical-ft-stage3-dpo with Docker Model Runner:
```
docker model run hf.co/vadimbelsky/qwen3.5-medical-ft-stage3-dpo
```

vadimbelsky commited on Mar 26

Commit

b958f59

verified ·

1 Parent(s): 650a910

Remove Haiku mentions from model card

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -59,7 +59,7 @@ Given an ED SOAP intake note, the model outputs a structured triage decision:
 |--------|-------------|-----------|--------|---------|
 | A | Anti-overtriage synthetic (ESI 3→1/2 rejected) | 2,388 | 1× | 2,388 |
 | B | Anti-overtriage synthetic (ESI 4/5→1/2 rejected) | 1,500 | 1× | 1,500 |
-| C / C-haiku | Edge cases (Claude Haiku generated) | 39 | 1× | 39 |
 | D | ESI 1/2 anchor pairs (high-risk recall preservation) | 890 | 3× | 2,670 |
 | E-over | ESI 3 bidirectional — anti-overtriage | 297 | 2× | 594 |
 | E-under | ESI 3 bidirectional — anti-undertriage | 299 | 2× | 598 |

 |--------|-------------|-----------|--------|---------|
 | A | Anti-overtriage synthetic (ESI 3→1/2 rejected) | 2,388 | 1× | 2,388 |
 | B | Anti-overtriage synthetic (ESI 4/5→1/2 rejected) | 1,500 | 1× | 1,500 |
+| C | Edge cases (synthetic boundary scenarios) | 39 | 1× | 39 |
 | D | ESI 1/2 anchor pairs (high-risk recall preservation) | 890 | 3× | 2,670 |
 | E-over | ESI 3 bidirectional — anti-overtriage | 297 | 2× | 594 |
 | E-under | ESI 3 bidirectional — anti-undertriage | 299 | 2× | 598 |