Instructions to use HuggingFaceM4/tiny-random-idefics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceM4/tiny-random-idefics with Transformers:

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

pipe = pipeline("image-text-to-text", model="HuggingFaceM4/tiny-random-idefics")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("HuggingFaceM4/tiny-random-idefics")
model = AutoModelForImageTextToText.from_pretrained("HuggingFaceM4/tiny-random-idefics")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceM4/tiny-random-idefics with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceM4/tiny-random-idefics"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceM4/tiny-random-idefics",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceM4/tiny-random-idefics

SGLang

How to use HuggingFaceM4/tiny-random-idefics 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/tiny-random-idefics" \
    --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/tiny-random-idefics",
		"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/tiny-random-idefics" \
        --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/tiny-random-idefics",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceM4/tiny-random-idefics with Docker Model Runner:
```
docker model run hf.co/HuggingFaceM4/tiny-random-idefics
```

tiny-random-idefics

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{
  "additional_vocab_size": 2,
  "alpha_initializer": "ones",
  "alpha_type": "vector",
  "alphas_initializer_range": 0.0,
  "architectures": [
    "IdeficsForVisionText2Text"
  ],
  "bos_token_id": 1,
  "cross_layer_activation_function": "swiglu",
  "cross_layer_interval": 1,
  "dropout": 0.0,
  "eos_token_id": 2,
  "ffn_dim": 64,
  "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": 16,
  "initializer_range": 0.02,
  "intermediate_size": 11008,
  "max_new_tokens": 128,
  "max_position_embeddings": 128,
  "model_type": "idefics",
  "num_attention_heads": 4,
  "num_hidden_layers": 2,
  "pad_token_id": 0,
  "qk_layer_norms": false,
  "rms_norm_eps": 1e-06,
  "tie_word_embeddings": false,
  "torch_dtype": "float16",
  "transformers_version": "4.27.0.dev0",
  "use_cache": true,
  "use_resampler": true,
  "vocab_size": 32000,
  "word_embed_proj_dim": 16,
  "vision_config": {
    "hidden_act": "gelu",
    "embed_dim": 32,
    "image_size": 30,
    "intermediate_size": 37,
    "patch_size": 2,
    "num_attention_heads": 4,
    "num_hidden_layers": 5,
    "vision_model_name": "hf-internal-testing/tiny-random-clip"
  },
  "perceiver_config": {
    "qk_layer_norms_perceiver": false,
    "resampler_depth": 2,
    "resampler_head_dim": 8,
    "resampler_n_heads": 2,
    "resampler_n_latents": 16
  }
}