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, AutoModelForMultimodalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

VictorSanh commited on Aug 3, 2023

Commit

d464935

1 Parent(s): a2ee636

small wording issue

Browse files

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README.md +1 -1

README.md CHANGED Viewed

@@ -195,7 +195,7 @@ We use the following hyper and training parameters:
 We start from the base IDEFICS models and fine-tune the models by unfreezing all the parameters (vision encoder, language model, cross-attentions). The mixture is composed of following English datasets:
-| Data Source | Data Description                             | Number of unrepeated samples | Sampling ratio |
 |-------------|----------------------------------------------|------------------------------|----------------|
 | [M3IT](https://huggingface.co/datasets/MMInstruction/M3IT)                       | Prompted image-text academic datasets              | 1.5M  | 7.7%  |
 | [LRV-Instruction](https://huggingface.co/datasets/VictorSanh/LrvInstruction)     | Triplets of image/question/answer                  | 155K  | 1.7%  |

 We start from the base IDEFICS models and fine-tune the models by unfreezing all the parameters (vision encoder, language model, cross-attentions). The mixture is composed of following English datasets:
+| Data Source | Data Description                             | Number of Unique Samples | Sampling ratio |
 |-------------|----------------------------------------------|------------------------------|----------------|
 | [M3IT](https://huggingface.co/datasets/MMInstruction/M3IT)                       | Prompted image-text academic datasets              | 1.5M  | 7.7%  |
 | [LRV-Instruction](https://huggingface.co/datasets/VictorSanh/LrvInstruction)     | Triplets of image/question/answer                  | 155K  | 1.7%  |