Image-Text-to-Text
Transformers
Safetensors
English
idefics3
text-generation
documents
code
formula
chart
ocr
layout
table
document-parse
docling
granite
extraction
math
conversational
Instructions to use ibm-granite/granite-docling-258M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibm-granite/granite-docling-258M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ibm-granite/granite-docling-258M") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("ibm-granite/granite-docling-258M") model = AutoModelForMultimodalLM.from_pretrained("ibm-granite/granite-docling-258M", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ibm-granite/granite-docling-258M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ibm-granite/granite-docling-258M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ibm-granite/granite-docling-258M", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/ibm-granite/granite-docling-258M
- SGLang
How to use ibm-granite/granite-docling-258M 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 "ibm-granite/granite-docling-258M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ibm-granite/granite-docling-258M", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "ibm-granite/granite-docling-258M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ibm-granite/granite-docling-258M", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use ibm-granite/granite-docling-258M with Docker Model Runner:
docker model run hf.co/ibm-granite/granite-docling-258M
File size: 2,475 Bytes
2d59cb9 6298576 2d59cb9 6298576 2d59cb9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | {
"_flash_attn_2_enabled": true,
"architectures": [
"Idefics3ForConditionalGeneration"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 100264,
"eos_token_id": 100338,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 576,
"image_token_id": 100270,
"initializer_range": 0.02,
"intermediate_size": 1536,
"max_position_embeddings": 8192,
"mlp_bias": false,
"model_type": "idefics3",
"neftune_noise_alpha": 0.0,
"num_attention_heads": 9,
"num_hidden_layers": 30,
"num_key_value_heads": 3,
"pad_token_id": 128002,
"perceiver_config": {
"attention_dropout": 0.0,
"hidden_act": "silu",
"model_type": "vllama3",
"num_key_value_heads": 1,
"qk_layer_norms_perceiver": false,
"resampler_depth": 6,
"resampler_head_dim": 96,
"resampler_n_heads": 16,
"resampler_n_latents": 64
},
"pixel_shuffle_factor": 4,
"pretraining_tp": 1,
"qk_layer_norms": false,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 100000.0,
"scale_factor": 4,
"text_config": {
"architectures": [
"llama"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 100257,
"eos_token_id": 100257,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 576,
"initializer_range": 0.02,
"intermediate_size": 1536,
"max_position_embeddings": 8192,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 9,
"num_hidden_layers": 30,
"num_key_value_heads": 3,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 10000.0,
"torch_dtype": "bfloat16",
"use_cache": true,
"vocab_size": 100480
},
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.53.0.dev0",
"use_cache": true,
"use_resampler": false,
"vision_config": {
"attention_dropout": 0.0,
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 768,
"image_size": 512,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-06,
"max_image_size": {
"longest_edge": 512
},
"model_type": "idefics3_vision",
"num_attention_heads": 12,
"num_channels": 3,
"num_hidden_layers": 12,
"patch_size": 16,
"size": {
"longest_edge": 2048
},
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"use_base_siglip": true
},
"vocab_size": 100480
}
|