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

Libraries

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

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

idefics2-8b-chatty-AWQ

File size: 959 Bytes

9f9d792

{
  "architectures": [
    "Idefics2ForConditionalGeneration"
  ],
  "image_token_id": 32001,
  "model_type": "idefics2",
  "perceiver_config": {
    "model_type": "idefics2"
  },
  "quantization_config": {
    "bits": 4,
    "group_size": 128,
    "modules_to_not_convert": ["model.vision_model", "model.connector.modality_projection", "model.connector.perceiver_resampler"],
    "quant_method": "awq",
    "version": "gemm",
    "zero_point": true
  },
  "text_config": {
    "max_position_embeddings": 32768,
    "model_type": "mistral",
    "pad_token_id": 0,
    "rms_norm_eps": 1e-05,
    "vocab_size": 32003
  },
  "tie_word_embeddings": false,
  "torch_dtype": "float16",
  "transformers_version": "4.40.0.dev0",
  "use_cache": true,
  "vision_config": {
    "hidden_size": 1152,
    "image_size": 980,
    "intermediate_size": 4304,
    "model_type": "idefics2",
    "num_attention_heads": 16,
    "num_hidden_layers": 27,
    "patch_size": 14
  }
}