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

Libraries

How to use HuggingFaceM4/idefics2-8b-base-AWQ 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-AWQ")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

idefics2-8b-base-AWQ / README.md

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metadata

license: apache-2.0
datasets:
  - HuggingFaceM4/OBELICS
  - laion/laion-coco
  - wikipedia
  - facebook/pmd
  - pixparse/idl-wds
  - pixparse/pdfa-eng-wds
  - wendlerc/RenderedText
  - HuggingFaceM4/the_cauldron
  - teknium/OpenHermes-2.5
  - GAIR/lima
  - databricks/databricks-dolly-15k
  - meta-math/MetaMathQA
  - TIGER-Lab/MathInstruct
  - microsoft/orca-math-word-problems-200k
  - camel-ai/math
  - AtlasUnified/atlas-math-sets
  - tiedong/goat
  - Lin-Chen/ShareGPT4V
  - jxu124/llava_conversation_58k
language:
  - en
tags:
  - multimodal
  - vision
  - image-text-to-text
  - quantized
  - 4-bit
  - AWQ

4-bit AWQ-quantized version of HuggingFaceM4/idefics2-8b-base. Refer to the original model's card for more information (including inference snippet).