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dmusingu
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Model Details
Model Provider
dmusingu
Model Tree
Qwen/Qwen3-VL-8B-Instruct
Input Modalities
Output Modalities
Supported Functionality
The model was trained with supervised fine-tuning (SFT) using TRL. Training samples were selected from the MIMIC-CXR dataset, which contains frontal and lateral chest radiographs paired with structured radiology reports.
Training details:
| Base model | Qwen/Qwen3-VL-8B-Instruct |
| Training framework | TRL 0.26.2 (SFTTrainer) |
| Epochs | 2 |
| Total steps | 7 120 |
| Eval loss | 0.428 |
| Eval token accuracy | ~88% |
| Precision | bfloat16 |
| Hardware | 1× NVIDIA A100 |
python
from transformers import AutoProcessor, Qwen2VLForConditionalGenerationfrom qwen_vl_utils import process_vision_infoimport torch model = Qwen2VLForConditionalGeneration.from_pretrained( "dmusingu/qwen3-vl-8b-mimic-cxr-sft", torch_dtype=torch.bfloat16, device_map="auto",)processor = AutoProcessor.from_pretrained("denmus/qwen3-vl-8b-mimic-cxr-sft") messages = [ { "role": "user", "content": [ {"type": "image", "image": "<path_or_url_to_cxr>"}, {"type": "text", "text": "Describe the findings in this chest X-ray."}, ], }] 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, return_tensors="pt").to(model.device) output_ids = model.generate(**inputs, max_new_tokens=256)output = processor.batch_decode(output_ids[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)print(output[0])MIMIC-CXR is a credentialed dataset. Access requires PhysioNet registration and completion of the required training at physionet.org/content/mimic-cxr.
If you use this model, please cite MIMIC-CXR:
bibtex
@article{johnson2019mimic, title = {MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports}, author = {Johnson, Alistair EW and Pollard, Tom J and Berkowitz, Seth J and others}, journal = {Scientific data}, volume = {6}, number = {1}, pages = {317}, year = {2019}, publisher = {Nature Publishing Group}}from transformers import AutoProcessor, Qwen2VLForConditionalGenerationfrom qwen_vl_utils import process_vision_infoimport torch model = Qwen2VLForConditionalGeneration.from_pretrained( "dmusingu/qwen3-vl-8b-mimic-cxr-sft", torch_dtype=torch.bfloat16, device_map="auto",)processor = AutoProcessor.from_pretrained("denmus/qwen3-vl-8b-mimic-cxr-sft") messages = [ { "role": "user", "content": [ {"type": "image", "image": "<path_or_url_to_cxr>"}, {"type": "text", "text": "Describe the findings in this chest X-ray."}, ], }] 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, return_tensors="pt").to(model.device) output_ids = model.generate(**inputs, max_new_tokens=256)output = processor.batch_decode(output_ids[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)print(output[0])@article{johnson2019mimic, title = {MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports}, author = {Johnson, Alistair EW and Pollard, Tom J and Berkowitz, Seth J and others}, journal = {Scientific data}, volume = {6}, number = {1}, pages = {317}, year = {2019}, publisher = {Nature Publishing Group}}