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ottokevin
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Model Details
Model Provider
ottokevin
Model Tree
Qwen/Qwen2.5-VL-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Metric | Value |
|---|---|
| Accuracy | 78.1% |
| Weighted F1 | 0.74 |
| Macro F1 | 0.45 |
| No DR F1 | 0.955 |
| Moderate F1 | 0.708 |
| Avg Latency (GPU) | 1.16s |
python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessorimport torch model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "ottokevin/JUSTCSL-OphthaVision-3B", torch_dtype=torch.bfloat16, device_map="auto",)processor = AutoProcessor.from_pretrained("ottokevin/JUSTCSL-OphthaVision-3B") # Prepare image + promptfrom PIL import Imageimage = Image.open("retina.png").convert("RGB")messages = [ { "role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": "Classify this retina image: 0=No DR, 1=Mild, 2=Moderate, 3=Severe, 4=Proliferative DR. Answer:"}, ], },]text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = processor(text=[text], images=[image], padding=True, return_tensors="pt").to(model.device)generated = model.generate(**inputs, max_new_tokens=8, do_sample=False)response = processor.tokenizer.decode(generated[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(response)from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessorimport torch model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "ottokevin/JUSTCSL-OphthaVision-3B", torch_dtype=torch.bfloat16, device_map="auto",)processor = AutoProcessor.from_pretrained("ottokevin/JUSTCSL-OphthaVision-3B") # Prepare image + promptfrom PIL import Imageimage = Image.open("retina.png").convert("RGB")messages = [ { "role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": "Classify this retina image: 0=No DR, 1=Mild, 2=Moderate, 3=Severe, 4=Proliferative DR. Answer:"}, ], },]text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = processor(text=[text], images=[image], padding=True, return_tensors="pt").to(model.device)generated = model.generate(**inputs, max_new_tokens=8, do_sample=False)response = processor.tokenizer.decode(generated[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(response)