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

Libraries

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

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b-base")
model = AutoModelForMultimodalLM.from_pretrained("HuggingFaceM4/idefics2-8b-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceM4/idefics2-8b-base"
# 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-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

VictorSanh commited on Apr 12, 2024

Commit

dff8ac8

1 Parent(s): 2570852

even more friendly

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -70,7 +70,7 @@ IDEFICS-2 exhibits strong performance for a model of its size (8B parameters) wh
 <details><summary>For more details, expand the result table.</summary>
-| <nobr>Model</nobr>        | <nobr>Open weights</nobr> | <nobr>Size</nobr> | <nobr># tokens per image</nobr> | <nobr>MMMU <br>(val/test)</nobr>   | <nobr>MathVista <br>(testmini)</nobr> | <nobr>TextVQA <br>(val)</nobr> | <nobr>MMBench <br>(test)</nobr> | <nobr>VQAv2 <br>(test-dev)</nobr> | <nobr>DocVQA <br>(test)</nobr> |
 |--------------|-------------|------|--------------------|-----------|-----------|---------|---------|---------|---------|
 | [DeepSeek-VL](https://huggingface.co/deepseek-ai/deepseek-vl-7b-chat)  | ✅ |  7B   | 576                | 36.6/-   | 36.1      | -       | 73.2    |  -     |   -   |
 | [LLaVa-NeXT-13B](https://huggingface.co/liuhaotian/llava-v1.6-vicuna-13b)   | ✅ | 13B  | 2880               | 36.2/-   | 35.3      | 67.1    | 70.0  | 82.8 |   -   |

 <details><summary>For more details, expand the result table.</summary>
+| <nobr>Model</nobr>        | <nobr>Open <br>weights</nobr> | <nobr>Size</nobr> | <nobr># tokens <br>per image</nobr> | <nobr>MMMU <br>(val/test)</nobr>   | <nobr>MathVista <br>(testmini)</nobr> | <nobr>TextVQA <br>(val)</nobr> | <nobr>MMBench <br>(test)</nobr> | <nobr>VQAv2 <br>(test-dev)</nobr> | <nobr>DocVQA <br>(test)</nobr> |
 |--------------|-------------|------|--------------------|-----------|-----------|---------|---------|---------|---------|
 | [DeepSeek-VL](https://huggingface.co/deepseek-ai/deepseek-vl-7b-chat)  | ✅ |  7B   | 576                | 36.6/-   | 36.1      | -       | 73.2    |  -     |   -   |
 | [LLaVa-NeXT-13B](https://huggingface.co/liuhaotian/llava-v1.6-vicuna-13b)   | ✅ | 13B  | 2880               | 36.2/-   | 35.3      | 67.1    | 70.0  | 82.8 |   -   |