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Model: MedVLSynther/MedVLSynther-7B-RL_10K Source: Original Platform
2026-06-10 04:40:17 +08:00
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+---
+license: apache-2.0
+datasets:
+- MedVLSynther/MedSynVQA-10K
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
+---
+
+# MedVLSynther-7B-RL_10K
+
+Code: https://github.com/UCSC-VLAA/MedVLSynther
+Project Page: https://ucsc-vlaa.github.io/MedVLSynther/
+
+## Model Description
+
+MedVLSynther-7B-RL_10K is a 7B parameter medical vision-language model based on Qwen2.5-VL. 
+This model has been trained using reinforcement learning on MedSynVQA-10K dataset.
+
+## Model Details
+
+- **Base Model**: Qwen/Qwen2.5-VL-7B-Instruct
+- **Model Size**: 7B parameters
+- **Training Method**: Reinforcement Learning
+- **Training Data**: MedSynVQA-10K dataset
+
+## Usage
+
+Check here for demo images: https://github.com/UCSC-VLAA/MedVLSynther?tab=readme-ov-file#-quick-start
+
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import torch
+
+# Load the model
+model_name="MedVLSynther/MedVLSynther-7B-RL_10K"
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+processor = AutoProcessor.from_pretrained(model_name)
+
+# Example usage
+messages_1 = [
+    {
+        "role": "system",
+        "content": "You will solve a problem/request. You should provide your thoughts within <think> </think> tags before providing the answer.\nWrite your final answer within <answer> </answer> tags.",
+    },
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "assets/7bMMMU.png",
+            },
+            {"type": "text", "text": "This line of of myelinated axons in layer IV of visual cortex represents the axons of cells in the Choices: (A) Superior colliculus. (B) Lateral geniculate.(C) Retina. (D) Medial geniculate."},
+        ],
+    }
+]
+
+messages_2 = [
+    {
+        "role": "system",
+        "content": "You will solve a problem/request. You should provide your thoughts within <think> </think> tags before providing the answer.\nWrite your final answer within <answer> </answer> tags.",
+    },
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "assets/7bslake.png",
+            },
+            {"type": "text", "text": "Does the picture contain kidney? Choices: (A) Yes (B) No"},
+        ],
+    }
+]
+
+# Preparation for inference
+messages = messages_2
+
+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
+
+```bibtex
+@article{MedVLSynther,
+  title={MedVLSynther: Synthesizing High-Quality Visual Question Answering from Medical Documents with Generator-Verifier LMMs},
+  author={Huang, Xiaoke and Wang, Ningsen and Liu, Hui and Tang, Xianfeng and Zhou, Yuyin},
+  journal={arXiv preprint arXiv:2510.25867},
+  year={2025}
+}
+@article{MedVLThinker,
+  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 arXiv:2508.02669},
+  year={2025}
+}
+```
+
+## License
+
+This model is released under the Apache 2.0 license.