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
```

idefics-80b / update_all_models_readmes.sh

Leyo

update_all_readme_script (#15)

47f3ffb almost 3 years ago

raw

history blame contribute delete

1.03 kB

	# get latest version of "idefics-80b" repo
	git pull

	cd ..
	PATH_TO_REPOS=$(pwd)
	echo ${PATH_TO_REPOS}
	SOURCE_REPO="idefics-80b"

	if [ ! -d $SOURCE_REPO ]; then
	GIT_LFS_SKIP_SMUDGE=1 git clone "https://huggingface.co/HuggingFaceM4/${SOURCE_REPO}"
	else
	echo "Repository is already cloned."
	fi
	TARGET_REPOS=("idefics-9b" "idefics-9b-instruct" "idefics-80b-instruct")

	for TARGET_REPO in "${TARGET_REPOS[@]}"; do
	echo $TARGET_REPO
	if [ ! -d $TARGET_REPO ]; then
	GIT_LFS_SKIP_SMUDGE=1 git clone "https://huggingface.co/HuggingFaceM4/${TARGET_REPO}"
	else
	echo "Repository is already cloned."
	fi

	cd "$TARGET_REPO" \|\| exit

	# Make sure you have the latest version
	git pull

	# Remove the existing README
	rm -f README.md

	# Copy README from SOURCE_REPO
	cp "${PATH_TO_REPOS}/${SOURCE_REPO}/README.md" "${PATH_TO_REPOS}/${TARGET_REPO}"

	git add README.md
	git commit -m "Update README from ${SOURCE_REPO}"
	git push origin main

	cd ..
	done