How to use (#12)

- How to use (cf77a55ece4db350de7357f6b76e2f6975219b9b)
- Update README.md (c07538fc986849b59fa5773c1b0881118cab05c6)

README.md

@@ -246,6 +246,57 @@ The Llama 3.2 model collection also supports the ability to leverage the outputs
 
 **Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
 
+## How to use
+
+This repository contains two versions of Llama-3.2-11B-Vision-Instruct, for use with transformers and with the original `llama` codebase.
+
+### Use with transformers
+
+Starting with transformers >= 4.45.0 onward, you can run inference using conversational messages that may include an image you can query about.
+
+Make sure to update your transformers installation via `pip install --upgrade transformers`.
+
+```python
+import requests
+import torch
+from PIL import Image
+from transformers import MllamaForConditionalGeneration, AutoProcessor
+
+model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+
+model = MllamaForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+processor = AutoProcessor.from_pretrained(model_id)
+
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+messages = [
+    {"role": "user", "content": [
+        {"type": "image"},
+        {"type": "text", "text": "If I had to write a haiku for this one, it would be: "}
+    ]}
+]
+input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
+inputs = processor(image, input_text, return_tensors="pt").to(model.device)
+
+output = model.generate(**inputs, max_new_tokens=30)
+print(processor.decode(output[0]))
+```
+
+### Use with `llama`
+
+Please, follow the instructions in the [repository](https://github.com/meta-llama/llama).
+
+To download the original checkpoints, you can use `huggingface-cli` as follows:
+
+```
+huggingface-cli download meta-llama/Llama-3.2-11B-Vision-Instruct --include "original/*" --local-dir Llama-3.2-11B-Vision-Instruct
+```
+
 ## Hardware and Software
 
 **Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.