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
```

Leyo commited on Aug 18, 2023

Commit

ddca285

1 Parent(s): 8a29e64

add script to update readmes

Browse files

Files changed (1) hide show

update_all_models_readmes.sh +40 -0

update_all_models_readmes.sh ADDED Viewed

	@@ -0,0 +1,40 @@

+# 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