Instructions to use SandLogicTechnologies/granite-docling-258M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SandLogicTechnologies/granite-docling-258M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SandLogicTechnologies/granite-docling-258M-GGUF",
	filename="granite-docling-258M_IQ3_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SandLogicTechnologies/granite-docling-258M-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SandLogicTechnologies/granite-docling-258M-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf SandLogicTechnologies/granite-docling-258M-GGUF:IQ3_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SandLogicTechnologies/granite-docling-258M-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf SandLogicTechnologies/granite-docling-258M-GGUF:IQ3_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf SandLogicTechnologies/granite-docling-258M-GGUF:IQ3_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/granite-docling-258M-GGUF:IQ3_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf SandLogicTechnologies/granite-docling-258M-GGUF:IQ3_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/granite-docling-258M-GGUF:IQ3_M

Use Docker

docker model run hf.co/SandLogicTechnologies/granite-docling-258M-GGUF:IQ3_M

LM Studio
Jan
Ollama
How to use SandLogicTechnologies/granite-docling-258M-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/granite-docling-258M-GGUF:IQ3_M
```

Unsloth Studio

How to use SandLogicTechnologies/granite-docling-258M-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/granite-docling-258M-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/granite-docling-258M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SandLogicTechnologies/granite-docling-258M-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use SandLogicTechnologies/granite-docling-258M-GGUF with Docker Model Runner:
```
docker model run hf.co/SandLogicTechnologies/granite-docling-258M-GGUF:IQ3_M
```

Lemonade

How to use SandLogicTechnologies/granite-docling-258M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/granite-docling-258M-GGUF:IQ3_M

Run and chat with the model

lemonade run user.granite-docling-258M-GGUF-IQ3_M

List all available models

lemonade list

Granite-Docling-258M

Granite-Docling-258M is a compact vision-language document model developed by IBM, designed for OCR, document conversion, layout understanding, and structured content extraction workflows. This repository contains GGUF quantized variants of the model optimized for efficient local inference using llama.cpp.

The model specializes in transforming complex documents into machine-readable representations while preserving semantic structure, document hierarchy, tables, forms, equations, code blocks, and layout relationships. The quantized formats significantly reduce memory requirements while maintaining strong document-processing capabilities, enabling deployment on consumer hardware, edge devices, and enterprise document-processing pipelines.

Model Overview

Model Name: Granite-Docling-258M
Base Model: ibm-granite/granite-docling-258M
Architecture: Multimodal Document Understanding Model
Parameter Count: 258 Million
Modalities: Text, Image
Primary Languages: English
Developer: IBM
License: Apache 2.0

Quantization Formats

This repository provides various GGUF quantized versions of the Granite-Docling-258M model optimized for efficient local inference using llama.cpp. Below are the details of the available quantization formats.

IQ3_M

Size reduction of approx 62.97% (117 MB) compared to 16-bit (316 MB)
Aggressive 3-bit quantization optimized for maximum memory efficiency
Suitable for lightweight OCR pipelines and low-memory deployment environments
Enables practical execution of document-understanding workloads on consumer hardware
Complex layouts, dense tables, mathematical content, and highly structured extraction tasks may experience reduced fidelity compared to higher-precision variants

IQ4_NL

Size reduction of approx 62.34% (119 MB) compared to 16-bit (316 MB)
Advanced 4-bit non-linear quantization designed to better preserve OCR quality and document understanding performance
Better suited for structured document conversion, layout-aware analysis, and information extraction workflows
Designed to reduce quantization loss compared to more aggressive formats
May require slightly increased computational overhead during inference

IQ4_XS

Size reduction of approx 62.66% (118 MB) compared to 16-bit (316 MB)
Balanced 4-bit quantization focused on efficient inference and dependable document-processing performance
Provides a practical balance between memory efficiency, extraction quality, and runtime speed
Suitable for PDF conversion, OCR workflows, document digitization, and structured content extraction
Maintains stable performance across most practical document-understanding tasks

Q6_K

Size reduction of approx 48.42% (163 MB) compared to 16-bit (316 MB)
Higher-precision 6-bit K-Quant format designed to preserve document structure recognition and extraction quality
Better suited for demanding workloads involving complex layouts, tables, equations, technical documents, and dense visual content
Provides stronger output consistency and improved retention of the original model capabilities compared to lower-bit formats
Requires higher memory resources but offers enhanced document-conversion fidelity and structured extraction quality

Training Background (Original Model)

Granite-Docling-258M is trained with an emphasis on document intelligence, OCR, layout understanding, and structured document conversion across diverse document formats.

Pretraining

Large-scale multimodal pretraining across document-centric image and text datasets
Focus on text recognition, layout understanding, and document representation learning
Optimized for downstream OCR, document conversion, and document-analysis workloads

Document Understanding Optimization

Optimized for OCR, document parsing, and structured content extraction
Enhanced for preserving document hierarchy, layout relationships, and semantic structure
Improved performance on tables, forms, equations, code blocks, and technical documents
Designed to generate structured document representations suitable for downstream processing workflows

Key Capabilities

Optical Character Recognition (OCR) Extracts textual information from scanned documents, images, and visual content.
Document Understanding Interprets document structure, context, and relationships between textual elements.
Layout Analysis Understands page layouts, sections, tables, forms, and structured document formats.
Structured Information Extraction Supports extraction of key-value pairs, fields, metadata, and document content.
Document Conversion Converts complex documents into structured machine-readable representations.
Efficient Local Deployment Quantized variants enable practical document-processing inference on consumer hardware.

Usage Example

Using llama.cpp

./llama-mtmd-cli \
  -m SandlogicTechnologies/Granite-Docling-258M_IQ4_NL.gguf \
  --mmproj SandlogicTechnologies/mmproj-granite-docling-258M-f16.gguf \
  --image document.png \
  -p "Extract all tables and convert the document into structured markdown."

Recommended Usecases

Document Digitization Convert scanned documents into searchable and structured formats.
OCR Pipelines Extract text from images, scanned pages, and document archives.
Structured Content Extraction Extract tables, forms, metadata, and key-value information from documents.
Enterprise Document Processing Automate large-scale document conversion and information retrieval workflows.
RAG Data Preparation Transform enterprise documents into structured formats suitable for retrieval systems.
Research and Experimentation Evaluate OCR, layout analysis, and document-understanding workflows.

Acknowledgments

These quantized models are based on the original work by the IBM Granite development team.

Special thanks to:

The IBM team for developing and releasing the Granite-Docling-258M model.
Georgi Gerganov and the llama.cpp open-source community for enabling efficient quantization and inference via the GGUF format.

Contact

For questions, feedback, or support, please reach out at support@sandlogic.com or visit https://www.sandlogic.com/

Downloads last month: 137

GGUF

Model size

0.2B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

6-bit

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Model tree for SandLogicTechnologies/granite-docling-258M-GGUF

Base model

ibm-granite/granite-docling-258M

Quantized

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