Instructions to use mmrech/pitvqa-qwen2vl-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mmrech/pitvqa-qwen2vl-merged with Transformers:

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

pipe = pipeline("image-text-to-text", model="mmrech/pitvqa-qwen2vl-merged")
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("mmrech/pitvqa-qwen2vl-merged")
model = AutoModelForMultimodalLM.from_pretrained("mmrech/pitvqa-qwen2vl-merged")
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 mmrech/pitvqa-qwen2vl-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mmrech/pitvqa-qwen2vl-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mmrech/pitvqa-qwen2vl-merged",
		"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/mmrech/pitvqa-qwen2vl-merged

SGLang

How to use mmrech/pitvqa-qwen2vl-merged 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 "mmrech/pitvqa-qwen2vl-merged" \
    --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": "mmrech/pitvqa-qwen2vl-merged",
		"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 "mmrech/pitvqa-qwen2vl-merged" \
        --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": "mmrech/pitvqa-qwen2vl-merged",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use mmrech/pitvqa-qwen2vl-merged with Docker Model Runner:
```
docker model run hf.co/mmrech/pitvqa-qwen2vl-merged
```

PitVQA Merged Model

A ready-to-use version of PitVQA with the Stage 4 (Unified) adapter merged into the base weights. No adapter loading required - just load and run.

Why Use This Model?

Feature	This (Merged)	Adapter Version
Setup complexity	Simple	Requires PEFT
Load time	Faster	Slower
Flexibility	Single task mode	Switch adapters
Best for	Production deployment	Research/experimentation

Quick Start

from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
import torch

# Load merged model directly - no adapter loading needed!
model = Qwen2VLForConditionalGeneration.from_pretrained(
    "mmrech/pitvqa-qwen2vl-merged",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("mmrech/pitvqa-qwen2vl-merged")

# Run inference
from PIL import Image
image = Image.open("surgical_frame.jpg")

messages = [{"role": "user", "content": [
    {"type": "image", "image": image},
    {"type": "text", "text": "Point to the suction device in this surgical image."}
]}]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

output = model.generate(**inputs, max_new_tokens=128)
print(processor.decode(output[0], skip_special_tokens=True))
# Output: <point x='75.8' y='75.1'>suction device</point>

With 4-bit Quantization (Lower Memory)

from transformers import BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

model = Qwen2VLForConditionalGeneration.from_pretrained(
    "mmrech/pitvqa-qwen2vl-merged",
    quantization_config=bnb_config,
    device_map="auto"
)

Supported Tasks

This merged model supports ALL tasks from Stage 4 training:

Point Localization

Prompt: "Point to the suction device in this surgical image."
Output: <point x='75.8' y='75.1'>suction device</point>

Bounding Box Detection

Prompt: "Draw a bounding box around the tumor region."
Output: <box x1='30' y1='30' x2='70' y2='70'>tumor region</box>

Phase Classification

Prompt: "What surgical phase is shown?"
Output: sellar phase

Supported phases: nasal, sellar, tumor_removal, closure

Free-form Queries

Prompt: "What instruments are visible in this surgical scene?"
Output: The image shows a suction device positioned in the surgical field...

Model Details

Source: Merged from mmrech/pitvqa-qwen2vl-unified-v2 Stage 4 adapter
Base: Qwen/Qwen2-VL-2B-Instruct
Parameters: ~2B (full model, adapter merged)
Training: SFT with LoRA, then merged with merge_and_unload()

Related Resources

Adapter Version: pitvqa-qwen2vl-unified-v2 (for multi-stage switching)
Dataset: pitvqa-comprehensive-spatial
Demo: HuggingFace Space

Citation

@misc{pitvqa2026,
  title={PitVQA: Multi-Task Vision-Language Model for Pituitary Surgery},
  author={Matheus Rech},
  year={2026},
  url={https://huggingface.co/mmrech/pitvqa-qwen2vl-merged}
}

License

Apache 2.0

Downloads last month: 8

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for mmrech/pitvqa-qwen2vl-merged

Base model

Qwen/Qwen2-VL-2B

Finetuned

Qwen/Qwen2-VL-2B-Instruct