Qwen3-VL-4B-bnb-nf4

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Qwen3-VL-4B Model quantised by bitsandbytes NF4 Quantisation Method

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Direct Use

model_4bit.eval()

model_4bit.model.compile( mode="reduce-overhead", fullgraph=False, )

processor = AutoProcessor.from_pretrained( "Qwen/Qwen3-VL-4B-Instruct", min_pixels=256 * 32 * 32, max_pixels=1280 * 32 * 32, )

messages = [ { "role": "user", "content": [ {"type": "image", "image": "/content/IMG_2680.png"}, { "type": "text", "text": "Please extract the handwritten chess moves from this chess notation sheet.", }, ], } ]

inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", enable_thinking=False, ).to(model_4bit.device)

with torch.no_grad(): generated_ids = model_4bit.generate( **inputs, max_new_tokens=200, do_sample=False, cache_implementation="static", )

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[0])

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