Instructions to use meta-llama/Llama-3.2-11B-Vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meta-llama/Llama-3.2-11B-Vision with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="meta-llama/Llama-3.2-11B-Vision")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("meta-llama/Llama-3.2-11B-Vision")
model = AutoModelForMultimodalLM.from_pretrained("meta-llama/Llama-3.2-11B-Vision", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meta-llama/Llama-3.2-11B-Vision with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meta-llama/Llama-3.2-11B-Vision"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meta-llama/Llama-3.2-11B-Vision",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/meta-llama/Llama-3.2-11B-Vision

SGLang

How to use meta-llama/Llama-3.2-11B-Vision with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "meta-llama/Llama-3.2-11B-Vision" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meta-llama/Llama-3.2-11B-Vision",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "meta-llama/Llama-3.2-11B-Vision" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meta-llama/Llama-3.2-11B-Vision",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use meta-llama/Llama-3.2-11B-Vision with Docker Model Runner:
```
docker model run hf.co/meta-llama/Llama-3.2-11B-Vision
```

Image representation interpretation

#71

by kanishka - opened Apr 8, 2025

Discussion

kanishka

Apr 8, 2025

•

edited Apr 8, 2025

Hi! I am interested in using the image representations from the Llama-3.2-11B-Vision model, and am unsure how exactly to interpret the output.

This is what I ran:

from PIL import Image
from transformers import MllamaForConditionalGeneration, AutoProcessor

model_id = "meta-llama/Llama-3.2-11B-Vision"

model = MllamaForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_id)

image_paths = ["../data/aardvark/aardvark_01b.jpg", "../data/aardvark/aardvark_02s.jpg", "../data/aardvark/aardvark_10s.jpg"] # change accordingly

img = [Image.open(open(ip, "rb")) for ip in image_paths]

encoded = processors(images=img, text=[" ", " ", ""], return_tensors='pt')
encoded.pop("input_ids")
encoded.pop("attention_mask")
encoded.pop("cross_attention_mask")
encoded = encoded.to(torch.bfloat16)
encoded = encoded.to("cuda:0")

output = model.vision_model(**encoded)

output.last_hidden_state.shape

# output: torch.Size([1, 3, 4, 1025, 7680])

I'm wondering how I should interpret this shape -- the 3 clearly is the batch; and 7680 is clearly the representation dimension. But I am unsure what the other shapes represent. I'd love to get some clarity on this

Thanks!

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