Text Generation
Transformers
PyTorch
Safetensors
English
idefics
image-text-to-text
multimodal
text
image
image-to-text
text-generation-inference
Instructions to use HuggingFaceM4/idefics-9b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceM4/idefics-9b-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceM4/idefics-9b-instruct")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics-9b-instruct") model = AutoModelForMultimodalLM.from_pretrained("HuggingFaceM4/idefics-9b-instruct") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HuggingFaceM4/idefics-9b-instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HuggingFaceM4/idefics-9b-instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuggingFaceM4/idefics-9b-instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HuggingFaceM4/idefics-9b-instruct
- SGLang
How to use HuggingFaceM4/idefics-9b-instruct 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/idefics-9b-instruct" \ --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/idefics-9b-instruct", "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/idefics-9b-instruct" \ --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/idefics-9b-instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HuggingFaceM4/idefics-9b-instruct with Docker Model Runner:
docker model run hf.co/HuggingFaceM4/idefics-9b-instruct
File size: 1,407 Bytes
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"_name_or_path": "None",
"additional_vocab_size": 3,
"alpha_initializer": "zeros",
"alpha_type": "float",
"alphas_initializer_range": 0.0,
"architectures": [
"IdeficsForVisionText2Text"
],
"bos_token_id": 1,
"cross_layer_activation_function": "swiglu",
"cross_layer_interval": 4,
"dropout": 0.0,
"eos_token_id": 2,
"freeze_lm_head": false,
"freeze_text_layers": false,
"freeze_text_module_exceptions": [],
"freeze_vision_layers": false,
"freeze_vision_module_exceptions": [],
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 11008,
"max_position_embeddings": 2048,
"max_sequence_length": 2048,
"model_type": "idefics",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"pad_token_id": 0,
"qk_layer_norms": true,
"rms_norm_eps": 1e-06,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.28.0.dev0",
"use_cache": true,
"use_resampler": true,
"vocab_size": 32000,
"vision_config": {
"hidden_act": "gelu",
"embed_dim": 1280,
"image_size": 224,
"intermediate_size": 5120,
"patch_size": 14,
"num_attention_heads": 16,
"num_hidden_layers": 32
},
"perceiver_config": {
"qk_layer_norms_perceiver": true,
"resampler_depth": 6,
"resampler_head_dim": 96,
"resampler_n_heads": 16,
"resampler_n_latents": 64
}
}
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