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

Libraries

How to use HuggingFaceM4/idefics-80b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceM4/idefics-80b-instruct")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics-80b-instruct")
model = AutoModelForImageTextToText.from_pretrained("HuggingFaceM4/idefics-80b-instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceM4/idefics-80b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceM4/idefics-80b-instruct"
# 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-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceM4/idefics-80b-instruct

SGLang

How to use HuggingFaceM4/idefics-80b-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 "HuggingFaceM4/idefics-80b-instruct" \
    --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-instruct",
		"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-instruct" \
        --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-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceM4/idefics-80b-instruct with Docker Model Runner:
```
docker model run hf.co/HuggingFaceM4/idefics-80b-instruct
```

How to give PIL image.

by monuminu - opened Aug 23, 2023

Discussion

monuminu

Aug 23, 2023

Hi All,

Thanks for the great work. How can i add a PIL image

VictorSanh

Aug 28, 2023

Hi!
The processor takes as inputs list of PIL image or texts. the texts can be either image URLs or standard query-texts.
In your case, you simply need to replace the image URL by a pil image!

monuminu

Aug 28, 2023

Thanks for TGI we can only use URL right ?? Is there any we can pass to TGI apart from URL ?

VictorSanh

Aug 28, 2023

got it! that's correct.
for tgi, as for now, we are not supporting image inputs per se. as a first step to allow models like idefics, image need to be passed as image markdowns (more specifically as ![](IMAGE_URL)) and the tgi backend will take care of parsing the IMAGE_URL and fetching the corresponding image.
you can have a look here: https://github.com/huggingface/text-generation-inference/blob/211b54ac41cae9a369f3d74bd6cc666ff4a0c526/server/text_generation_server/models/idefics_causal_lm.py#L129

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