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shravvvv
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Model Details
Model Provider
shravvvv
Model Tree
Qwen/Qwen3-VL-8B-Instruct
Input Modalities
Output Modalities
Supported Functionality
This is a LoRA adapter, so load the base model first and attach the adapter:
python
import torchfrom PIL import Imagefrom transformers import AutoModelForImageTextToText, AutoProcessorfrom peft import PeftModelBASE = "Qwen/Qwen3-VL-8B-Instruct"ADAPTER = "shravvvv/CVPD"model = AutoModelForImageTextToText.from_pretrained(BASE, dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(model, ADAPTER)processor = AutoProcessor.from_pretrained(ADAPTER)model.eval()image = Image.open("example.jpg").convert("RGB")messages = [{"role": "user", "content": [{"type": "image", "image": image},{"type": "text", "text": "What is the text written on the sign?"},]}]text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)with torch.inference_mode():out = model.generate(**inputs, max_new_tokens=128)print(processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])
Apache 2.0.
bibtex
@inproceedings{cvpd,author = {Venkatraman, Shravan and Thawakar, Omkar and Thawkar, Ritesh and Shaker, Abdelrahman and Muhammad, Rao},title = {Perception Before Supervision: Self-Contained Visual Distillation from Counterfactual Blind Spots},booktitle = {37th British Machine Vision Conference 2026, {BMVC} 2026, Lancaster, UK, November 23-26, 2026},publisher = {BMVA},year = {2026}}