Instructions to use HuggingFaceM4/idefics2-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceM4/idefics2-8b with Transformers:

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

pipe = pipeline("image-text-to-text", model="HuggingFaceM4/idefics2-8b")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b")
model = AutoModelForImageTextToText.from_pretrained("HuggingFaceM4/idefics2-8b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceM4/idefics2-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceM4/idefics2-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceM4/idefics2-8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceM4/idefics2-8b

SGLang

How to use HuggingFaceM4/idefics2-8b 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/idefics2-8b" \
    --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/idefics2-8b",
		"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/idefics2-8b" \
        --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/idefics2-8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceM4/idefics2-8b with Docker Model Runner:
```
docker model run hf.co/HuggingFaceM4/idefics2-8b
```

CUDA out of memory

#19

by karambos - opened Apr 18, 2024

Discussion

karambos

Apr 18, 2024

•

edited Apr 22, 2024

CUDA out of memory. Tried to allocate 12.78 GiB (GPU 0; 15.73 GiB total capacity; 11.21 GiB already allocated; 2.47 GiB free; 12.19 GiB reserved in total by PyTorch)

I have a cluster of GPU 4 GPU of 16GB,
GPU distribution (after model loading):
0: 9246/16300
1: 9246/16300
2: 9246/16300
3: 8038/16300

processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b", cache_dir = ".../.cache/huggingface/hub")
model = AutoModelForVision2Seq.from_pretrained("HuggingFaceM4/idefics2-8b", cache_dir = ".../.cache/huggingface/hub", device_map = "auto",, torch_dtype=torch.float16)

I changed this code so weight is distributed on GPU but

generated_ids = model.generate(**inputs, max_new_tokens=60)

GPU distribution (during running this code):
0: 12700/16300
1: 9246/16300
2: 9246/16300
3: 8038/16300

I am getting an error here: CUDA out of memory. Tried to allocate 12.78 GiB (GPU 0; 15.73 GiB total capacity; 11.21 GiB already allocated; 2.47 GiB free; 12.19 GiB reserved in total by PyTorch)

Thank you:)