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+# Llama 3.2 Vision
+
+The latest additions to Meta's family of foundation LLMs include multimodal vision/language models (VLMs) in 11B and 90B sizes with high-resolution image inputs (1120x1120) and cross-attention with base completion and instruction-tuned chat variants:
+
+* [`Llama-3.2-11B-Vision`](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision)
+* [`Llama-3.2-11B-Vision-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct)
+* [`Llama-3.2-90B-Vision`](https://huggingface.co/meta-llama/Llama-3.2-90B-Vision)
+* [`Llama-3.2-90B-Vision-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-90B-Vision-Instruct)
+
+While quantization and optimization efforts are underway, we have started with running the unquantized 11B model in a container based on HuggingFace Transformers that has been updated with the latest support for Llama-3.2-Vision a jump start on trying out these exciting new multimodal models - thanks to Meta for continuing to release open Llama models!
+
+!!! abstract "What you need"
+
+    1. One of the following Jetson devices:
+
+        <span class="blobDarkGreen4">Jetson AGX Orin (64GB)</span>
+        <span class="blobDarkGreen5">Jetson AGX Orin (32GB)</span>
+
+    2. Running one of the following versions of [JetPack](https://developer.nvidia.com/embedded/jetpack):
+
+        <span class="blobPink2">JetPack 6 (L4T r36)</span>
+
+    3. Sufficient storage space (preferably with NVMe SSD).
+
+        - `12.8GB` for `llama-vision` container image
+        - Space for models (`>25GB`)
+	
+    4. Clone and setup [`jetson-containers`](https://github.com/dusty-nv/jetson-containers/blob/master/docs/setup.md){:target="_blank"}:
+    
+		```bash
+		git clone https://github.com/dusty-nv/jetson-containers
+		bash jetson-containers/install.sh
+		``` 
+		
+    5. Request access to the gated models [here](https://huggingface.co/meta-llama) with your HuggingFace API key.
+
+		
+## Code Example
+
+Today Llama-3.2-11B-Vision is able to be run on Jetson AGX Orin in FP16 via HuggingFace Transformers.  Here's a simple code example from the model card for using it: 
+
+```python
+import time
+import requests
+import torch
+
+from PIL import Image
+from transformers import MllamaForConditionalGeneration, AutoProcessor
+
+model_id = "meta-llama/Llama-3.2-11B-Vision"
+model = MllamaForConditionalGeneration.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
+processor = AutoProcessor.from_pretrained(model_id)
+
+prompt = "<|image|><|begin_of_text|>If I had to write a haiku for this one"
+url = "https://llava-vl.github.io/static/images/view.jpg"
+raw_image = Image.open(requests.get(url, stream=True).raw)
+
+inputs = processor(text=prompt, images=raw_image, return_tensors="pt").to(model.device)
+output = model.generate(**inputs, do_sample=False, max_new_tokens=32)
+```
+
+<img src="https://llava-vl.github.io/static/images/view.jpg">
+
+```
+If I had to write a haiku for this one, it would be:
+
+A dock on a lake.
+A mountain in the distance.
+A long exposure.
+```
+
+Initial testing seems that Llama-3.2-Vision has more conversational abilities than VLMs typically retain after VQA alignment.  This [llama_vision.py](https://github.com/dusty-nv/jetson-containers/blob/master/packages/vlm/llama-vision/llama_vision.py) script has interactive completion and image loading to avoid re-loading the model.  It can be launched from the container like this:
+
+```bash
+jetson-containers run \
+    -e HUGGINGFACE_TOKEN=YOUR_API_KEY \
+    $(autotag llama-vision) \
+      python3 /opt/llama_vision.py \
+        --model "meta-llama/Llama-3.2-11B-Vision" \
+        --image "/data/images/hoover.jpg" \
+        --prompt "I'm out in the" \
+        --max-new-tokens 32 \
+        --interactive
+```
+
+After processing the initial [image](https://github.com/dusty-nv/jetson-containers/blob/master/data/images/hoover.jpg), it will ask you to submit another prompt or image:
+
+```
+total 4.8346s (39 tokens, 8.07 tokens/sec)
+
+Enter prompt or image path/URL:
+
+>> 
+```
+
+We will update this page and container as support for the Llama-3.2-Vision architecture is added to quantization APIs like MLC and llama.cpp for GGUF, which will reduce the memory and latency.
+
diff --git a/mkdocs.yml b/mkdocs.yml
index 524376f..666312b 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -94,6 +94,7 @@ nav:
       - LLaVA: tutorial_llava.md
       - Live LLaVA: tutorial_live-llava.md
       - NanoVLM: tutorial_nano-vlm.md
+      - Llama 3.2 Vision: llama_vlm.md
     - Vision Transformers (ViT): 
       - vit/index.md
       - EfficientViT: vit/tutorial_efficientvit.md