Update the model card

README.md

@@ -5,4 +5,47 @@ language:
 base_model:
 - meta-llama/Llama-3.2-11B-Vision-Instruct
 ---
+## Introduction
+This model originates from [Xkev/Llama-3.2V-11B-cot](https://huggingface.co/Xkev/Llama-3.2V-11B-cot). This repository simply quantizes the model into the NF4 format using the bitsandbytes library.
+All credit goes to the original repository.
+
+## Usage
+```
+from transformers import MllamaForConditionalGeneration, AutoProcessor, BitsAndBytesConfig
+from PIL import Image
+import time
+
+# Load model
+model_id = "zhangsongbo365/Llama-3.2V-11B-cot-nf4" 
+model = MllamaForConditionalGeneration.from_pretrained(
+    model_id,
+    use_safetensors=True,
+    device_map="cuda:0",
+    trust_remote_code=True
+)
+
+# Load tokenizer
+processor = AutoProcessor.from_pretrained(model_id)
+
+# Caption a local image
+IMAGE = Image.open("1.png").convert("RGB")  # 改为你的实际图片路径
+PROMPT = """<|begin_of_text|><|start_header_id|>user<|end_header_id|>
+Caption this image:
+<|image|><|eot_id|><|start_header_id|>assistant<|end_header_id|>
+"""
+
+inputs = processor(IMAGE, PROMPT, return_tensors="pt").to(model.device)
+prompt_tokens = len(inputs['input_ids'][0])
+print(f"Prompt tokens: {prompt_tokens}")
+
+t0 = time.time()
+generate_ids = model.generate(**inputs, max_new_tokens=256)
+t1 = time.time()
+total_time = t1 - t0
+generated_tokens = len(generate_ids[0]) - prompt_tokens
+time_per_token = generated_tokens/total_time
+print(f"Generated {generated_tokens} tokens in {total_time:.3f} s ({time_per_token:.3f} tok/s)")
+
+output = processor.decode(generate_ids[0][prompt_tokens:]).replace('<|eot_id|>', '')
+```