Update README.md

README.md

@@ -1,3 +1,50 @@
----
-license: llama3.2
----
+---
+license: llama3.2
+base_model:
+- meta-llama/Llama-3.2-11B-Vision-Instruct
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
+
+Converted from [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) using BitsAndBytes with NF4 (4-bit) quantization. Not using double quantization.
+Requires `bitsandbytes` to load.
+
+Example usage for image captioning:
+```python
+from transformers import MllamaForConditionalGeneration, AutoProcessor, BitsAndBytesConfig
+from PIL import Image
+import time
+
+# Load model
+model_id = "SeanScripts/Llama-3.2-11B-Vision-Instruct-nf4"
+model = MllamaForConditionalGeneration.from_pretrained(
+    model_id,
+    use_safetensors=True,
+    device_map="cuda:0"
+)
+# Load tokenizer
+processor = AutoProcessor.from_pretrained(model_id)
+
+# Caption a local image (could use a more specific prompt)
+IMAGE = Image.open("test.png").convert("RGB")
+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|>', '')
+print(output)
+
+```