Image-Text-to-Text
Transformers
Safetensors
PyTorch
mllama
facebook
meta
llama
llama-3
conversational
text-generation-inference
Instructions to use meta-llama/Llama-3.2-90B-Vision-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meta-llama/Llama-3.2-90B-Vision-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="meta-llama/Llama-3.2-90B-Vision-Instruct") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("meta-llama/Llama-3.2-90B-Vision-Instruct") model = AutoModelForImageTextToText.from_pretrained("meta-llama/Llama-3.2-90B-Vision-Instruct") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use meta-llama/Llama-3.2-90B-Vision-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "meta-llama/Llama-3.2-90B-Vision-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meta-llama/Llama-3.2-90B-Vision-Instruct", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/meta-llama/Llama-3.2-90B-Vision-Instruct
- SGLang
How to use meta-llama/Llama-3.2-90B-Vision-Instruct 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-3.2-90B-Vision-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meta-llama/Llama-3.2-90B-Vision-Instruct", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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-3.2-90B-Vision-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meta-llama/Llama-3.2-90B-Vision-Instruct", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use meta-llama/Llama-3.2-90B-Vision-Instruct with Docker Model Runner:
docker model run hf.co/meta-llama/Llama-3.2-90B-Vision-Instruct
🚩 Report: Illegal or restricted content
#33 opened 2 months ago
by
azabdev
Issues on noisy images
#30 opened over 1 year ago
by
elenapop
ValueError: Cross attention layer can't find neither `cross_attn_states` nor cached values for key/values!
1
#29 opened over 1 year ago
by
cheongmyeong17
How to use model across multiple GPUs
👍 1
#28 opened over 1 year ago
by
aswad546
The model does not support having a different number of images per batch?
1
#27 opened over 1 year ago
by
h1manshu
🚩 Report
#25 opened over 1 year ago
by
weizhengsuper
Request: DOI
1
#24 opened over 1 year ago
by
Madhuu77
"Your request to access this repo has been rejected by the repo's authors."
➕ 14
1
#19 opened over 1 year ago
by
Loie
Fine-tune Llama Vision models with TRL 🚀
🔥❤️ 6
2
#18 opened over 1 year ago
by
lewtun
Extracting language model only
3
#17 opened over 1 year ago
by
mariboo
Add widget examples
👍 1
#16 opened over 1 year ago
by
mishig
Training Data
❤️ 2
#15 opened over 1 year ago
by
JohnnieB