Instructions to use facebook/chameleon-30b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/chameleon-30b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="facebook/chameleon-30b")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("facebook/chameleon-30b") model = AutoModelForImageTextToText.from_pretrained("facebook/chameleon-30b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use facebook/chameleon-30b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "facebook/chameleon-30b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "facebook/chameleon-30b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/facebook/chameleon-30b
- SGLang
How to use facebook/chameleon-30b 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 "facebook/chameleon-30b" \ --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": "facebook/chameleon-30b", "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 "facebook/chameleon-30b" \ --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": "facebook/chameleon-30b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use facebook/chameleon-30b with Docker Model Runner:
docker model run hf.co/facebook/chameleon-30b
Meta Chameleon 30B
Repository for Meta Chameleon, a mixed-modal early-fusion foundation model from FAIR. See the Chameleon paper for more information.
The Chameleon collection on HuggingFace contains 7 billion parameter and 30 billion parameter model checkpoints.
[more details and usage examples coming soon]
Citation
To cite the paper, model, or software, please use the below:
@article{Chameleon_Team_Chameleon_Mixed-Modal_Early-Fusion_2024,
author = {Chameleon Team},
doi = {10.48550/arXiv.2405.09818},
journal = {arXiv preprint arXiv:2405.09818},
title = {Chameleon: Mixed-Modal Early-Fusion Foundation Models},
url = {https://github.com/facebookresearch/chameleon},
year = {2024}
}
License
Use of this repository and related resources are governed by the Chameleon Research License and this repository's LICENSE file.
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Repository for Meta Chameleon, a mixed-modal early-fusion foundation model from FAIR. • 2 items • Updated • 35