Image-Text-to-Text
Transformers
Safetensors
English
idefics2
multimodal
vision
text-generation-inference
Instructions to use HuggingFaceM4/idefics2-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| { | |
| "architectures": [ | |
| "Idefics2ForConditionalGeneration" | |
| ], | |
| "image_token_id": 32001, | |
| "model_type": "idefics2", | |
| "perceiver_config": { | |
| "model_type": "idefics2" | |
| }, | |
| "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": "float32", | |
| "transformers_version": "4.39.0.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 | |
| } | |
| } | |