Instructions to use HuggingFaceM4/idefics-80b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

HugoLaurencon commited on Aug 10, 2023

Commit

697ab43

1 Parent(s): 7ec80bb

Update README.md

Browse files

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README.md +5 -5

README.md CHANGED Viewed

@@ -9,7 +9,7 @@ datasets:
 - wikipedia
 - facebook/pmd
 - laion/laion2B-en
-- HuggingFaceM4/OBELISC
 ---
@@ -372,7 +372,7 @@ Besides, we also computed the classification accuracy on FairFace for both the b
 # License
-The model is built on top of of two pre-trained models: [laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K) and [huggyllama/llama-65b](https://huggingface.co/huggyllama/llama-65b). The first was released under an MIT license, while the second was released under a specific noncommercial license focused on research purposes. As such, users should comply with that license by applying directly to [Meta's form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform).
 We release the additional weights we trained under an MIT license.
@@ -381,8 +381,8 @@ We release the additional weights we trained under an MIT license.
 **BibTeX:**
 ```bibtex
-@misc{laurençon2023obelisc,
-      title={OBELISC: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents},
       author={Hugo Laurençon and Lucile Saulnier and Léo Tronchon and Stas Bekman and Amanpreet Singh and Anton Lozhkov and Thomas Wang and Siddharth Karamcheti and Alexander M. Rush and Douwe Kiela and Matthieu Cord and Victor Sanh},
       year={2023},
       eprint={2306.16527},
@@ -397,4 +397,4 @@ V, i, c, t, o, r, ,,  , S, t, a, s, ,,  , X, X, X
 # Model Card Contact
-Please open a discussion on the Community tab!

 - wikipedia
 - facebook/pmd
 - laion/laion2B-en
+- HuggingFaceM4/OBELICS
 ---
 # License
+The model is built on top of two pre-trained models: [laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K) and [huggyllama/llama-65b](https://huggingface.co/huggyllama/llama-65b). The first was released under an MIT license, while the second was released under a specific noncommercial license focused on research purposes. As such, users should comply with that license by applying directly to [Meta's form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform).
 We release the additional weights we trained under an MIT license.
 **BibTeX:**
 ```bibtex
+@misc{laurencon2023obelics,
+      title={OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents},
       author={Hugo Laurençon and Lucile Saulnier and Léo Tronchon and Stas Bekman and Amanpreet Singh and Anton Lozhkov and Thomas Wang and Siddharth Karamcheti and Alexander M. Rush and Douwe Kiela and Matthieu Cord and Victor Sanh},
       year={2023},
       eprint={2306.16527},
 # Model Card Contact
+Please open a discussion on the Community tab!