Text Generation
Transformers
PyTorch
Safetensors
English
idefics
image-text-to-text
multimodal
text
image
image-to-text
text-generation-inference
Instructions to use HuggingFaceM4/idefics-80b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceM4/idefics-80b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceM4/idefics-80b")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics-80b") model = AutoModelForImageTextToText.from_pretrained("HuggingFaceM4/idefics-80b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HuggingFaceM4/idefics-80b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HuggingFaceM4/idefics-80b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuggingFaceM4/idefics-80b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HuggingFaceM4/idefics-80b
- SGLang
How to use HuggingFaceM4/idefics-80b 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 "HuggingFaceM4/idefics-80b" \ --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": "HuggingFaceM4/idefics-80b", "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 "HuggingFaceM4/idefics-80b" \ --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": "HuggingFaceM4/idefics-80b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HuggingFaceM4/idefics-80b with Docker Model Runner:
docker model run hf.co/HuggingFaceM4/idefics-80b
Commit History
Update README.md 4a3d381
preprocessor_config 5215c66
Switch to IDEFICS/ OBELICS 62de87a
fix significant digits in table e23b75c
mention the useful tiny model de16541
rename 45dd546
rename a3796c6
add missing metadata 8ffb014
winoground fix 6edaaa6
IDEFICS 51b1776
take off remaining -1 c17962a
fix table results rounding 70fea44
switch to OE VQA acc 5cc3ed2
fix winoground numbers 0b721e1
Update result table 052d671
Update README.md 4c02d51
add eval tables 7d53dc8
new 0940a33
new 6c74def
Update README.md d3f1708
Update README.md a93d027
Update README.md 25ff6d2
Update README.md a8977b9
fix source num tokens fa62bff
add num tokens source 73fda5b
fix proportion numbers 436c345
Update README.md 2f0f4fc
Update README.md 8683d64
Update README.md ade3656
vs/path_2 (#2) de1ddb0
Fix table layout 08e31d6
first pass over the model card (#1) 66a38fb
metadata d85c074
metadata f070274
wip 61f2a7d
start c7e377b
start 382a0e6
initial commit 19b8bfc
Stas Bekman commited on