GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
microsoft
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
microsoft
Model Tree
Qwen/Qwen3-VL-8B-Instruct
Input Modalities
Output Modalities
Supported Functionality
We introduce UniRG-CXR, a radiology report generation model that obtains SOTA performance on ReXrank. More details can be found in the paper: Scaling medical imaging report generation with multimodal reinforcement learning
We recommend installing the transformers version with python=3.12 used in our experiments and other dependencies with this command:
markdown
pip install transformers==4.57.1 accelerate==1.12.0 torchvision==0.24.1 qwen-vl-utils==0.0.14
Below, we provide a some examples to show how to use UniRG-CXR with 🤗 Transformers or vLLM.
python
import torchfrom transformers import Qwen3VLForConditionalGeneration, AutoProcessorfrom qwen_vl_utils import process_vision_info# default: Load the model on the available device(s)model = Qwen3VLForConditionalGeneration.from_pretrained("microsoft/UniRG-CXR", dtype=torch.bfloat16, device_map="auto")# We recommend enabling flash_attention_2 for better acceleration and memory saving.# model = Qwen3VLForConditionalGeneration.from_pretrained(# "microsoft/UniRG-CXR",# dtype=torch.bfloat16,# attn_implementation="flash_attention_2",# device_map="auto",# )# You can set min_pixels and max_pixels according to your needs.min_pixels = 262144max_pixels = 262144processor = AutoProcessor.from_pretrained("microsoft/UniRG-CXR", min_pixels=min_pixels, max_pixels=max_pixels)messages = [{"role": "user","content": [{"type": "text", "text": "This is a radiology report generation task. Here is the context:"},{"type": "image","image": "<input your image path here>",},{"type": "text", "text": "Given the image and the context, directly provide the report in the following format:\nFindings: [write the findings] Impression: [write the impression]\nNow write the report in the format above."},],}]# Preparation for inferencetext = 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(device="cuda")# Inference: Generation of the outputgenerated_ids = model.generate(**inputs, max_new_tokens=4000)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)
Here we show an example of how to use UniRG-CXR with vLLM (tested with vllm==0.11.2 and transformers==4.57.1):
python
from vllm import LLM, SamplingParamsfrom transformers import AutoProcessormin_pixels = 262144max_pixels = 262144processor = AutoProcessor.from_pretrained("microsoft/UniRG-CXR", min_pixels=min_pixels, max_pixels=max_pixels)llm = LLM(model="microsoft/UniRG-CXR",trust_remote_code=True,dtype="bfloat16",max_model_len=8192,tensor_parallel_size=4,gpu_memory_utilization=0.8,limit_mm_per_prompt={"image": 1})# Set up sampling parameterssampling_params = SamplingParams(temperature=0.0,max_tokens=4000,)image_data = []image_data = ['Your image path']messages = [{"role": "user","content": [{"type": "text", "text": "This is a radiology report generation task. Here is the context:"},{"type": "image","image": image_data[0],},{"type": "text", "text": "Given the image and the context, directly provide the report in the following format:\nFindings: [write the findings] Impression: [write the impression]\nNow write the report in the format above."},],}]prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)if image_data:mm_prompt = {"prompt": prompt,"multi_modal_data": {"image": image_data}}else:mm_prompt = {"prompt": prompt}# Generate responseoutputs = llm.generate([mm_prompt], sampling_params)# Print the generated responsefor output in outputs:prompt = output.promptgenerated_text = output.outputs[0].textprint(f"Prompt: {prompt}")print(f"Generated text: {generated_text}")print("-" * 50)
If you find our work helpful, feel free to give us a cite.
markdown
@article{liu2026scaling,title={Scaling medical imaging report generation with multimodal reinforcement learning},author={Liu, Qianchu and Zhang, Sheng and Qin, Guanghui and Gu, Yu and Jin, Ying and Preston, Sam and Xu, Yanbo and Kiblawi, Sid and Yim, Wen-wai and Ossowski, Tim and others},journal={arXiv preprint arXiv:2601.17151},year={2026}}
Microsoft's Privacy Statement: https://go.microsoft.com/fwlink/?LinkId=521839.