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 Jul 10, 2023

Commit

08e31d6

1 Parent(s): 66a38fb

Fix table layout

Browse files

Files changed (1) hide show

README.md +6 -6

README.md CHANGED Viewed

@@ -113,12 +113,12 @@ We closel follow the training procedure layed out in [Flamingo](https://arxiv.or
 The model is trained on the following data mixture of openly accessible data:
-| Data Source | Type of Data | Number of Tokens in Source | Number of Images in Source | Epochs | Effective Proportion in Number of Tokens |
-|-------------|----------------------------|------------|----------------------------|--------|
-| PMD | Image-Text Pairs | TODO | TODO | 3 | 73.85% |
-| LAION | Image-Text Pairs | TODO | TODO | 1 | 6.15% |
-| OBELISC | Unstructured Multimodal Web Documents | TODO | TODO | 3 | 2.82% |
-| Wikipedia | Unstructured Multimodal Web Documents | TODO | TODO | 1 | 17.18% |
 For multimodal web documents, we feed the model sequences corresponding to the succession of text paragraphs and images.
 For image-text pairs, we form the training sequences by packing images with their captions.

 The model is trained on the following data mixture of openly accessible data:
+| Data Source | Type of Data                             | Number of Tokens in Source | Number of Images in Source | Epochs | Effective Proportion in Number of Tokens |
+|-------------|-----------------------------------------|---------------------------|---------------------------|--------|-----------------------------------------|
+| PMD         | Image-Text Pairs                         | TODO                      | TODO                      | 3      | 73.85%                                  |
+| LAION       | Image-Text Pairs                         | TODO                      | TODO                      | 1      | 6.15%                                   |
+| OBELISC     | Unstructured Multimodal Web Documents    | TODO                      | TODO                      | 3      | 2.82%                                   |
+| Wikipedia   | Unstructured Multimodal Web Documents    | TODO                      | TODO                      | 1      | 17.18%                                  |
 For multimodal web documents, we feed the model sequences corresponding to the succession of text paragraphs and images.
 For image-text pairs, we form the training sequences by packing images with their captions.