MedVLThinker-7B-RL_m23k
Code: https://github.com/UCSC-VLAA/MedVLThinker Project Page: https://ucsc-vlaa.github.io/MedVLThinker/
Model Description
MedVLThinker-7B-RL_m23k is a 7B parameter medical vision-language model based on Qwen2.5-VL. This model has been trained using reinforcement learning on Med23k dataset.
Model Details
- Base Model: Qwen/Qwen2.5-VL-7B-Instruct
- Model Size: 7B parameters
- Training Method: Reinforcement Learning
- Training Data: Med23k dataset
Usage
Check here for demo images: https://github.com/UCSC-VLAA/MedVLThinker?tab=readme-ov-file#demo
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
# Load the model
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"UCSC-VLAA/MedVLThinker-7B-RL_m23k",
torch_dtype=torch.bfloat16,
device_map="auto"
)
processor = AutoProcessor.from_pretrained("UCSC-VLAA/MedVLThinker-7B-RL_m23k")
# Example usage
messages = [
{
"role": "system",
"content": "You will solve a problem/request. You should provide your thoughts within <think> </think> tags before providing the answer. Write your final answer within <answer> </answer> tags.",
},
{
"role": "user",
"content": [
{
"type": "image",
"image": "path/to/medical/image.jpg",
},
{"type": "text", "text": "What can you see in this medical image?"},
],
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference
generated_ids = model.generate(**inputs, max_new_tokens=2048, temperature=0.6, top_p=0.95, do_sample=True)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
Citation
@article{medvlthinker2025,
title={MedVLThinker: Simple Baselines for Multimodal Medical Reasoning},
author={Huang, Xiaoke and Wu, Juncheng and Liu, Hui and Tang, Xianfeng and Zhou, Yuyin},
journal={arXiv preprint},
year={2025}
}
License
This model is released under the Apache 2.0 license.