granite-4.1-3b-GGUF

Granite-4.1-3B

Granite-4.1-3B is a compact instruction-tuned language model developed by IBM, designed for efficient conversational AI, reasoning, coding, retrieval-augmented generation (RAG), and enterprise NLP workloads. This repository contains GGUF quantized variants of the model optimized for efficient local inference using llama.cpp.

The model emphasizes strong instruction following, structured response generation, multilingual language understanding, and practical deployment efficiency while maintaining a significantly smaller footprint than larger Granite family models. Its compact architecture enables high-quality local inference across a wide variety of production AI workflows.

The quantized formats significantly reduce memory requirements while preserving language understanding and reasoning capability, making the model suitable for consumer hardware, edge deployments, and enterprise AI systems.

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Model Overview

• Model Name: Granite-4.1-3B
• Base Model: ibm-granite/granite-4.1-3b
• Architecture: Decoder-Only Transformer
• Parameter Count: Approximately 3 Billion Parameters
• Modalities: Text
• Languages: Multilingual
• Developer: IBM
• License: Apache 2.0

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Quantization Formats

This repository provides various GGUF quantized versions of the Granite-4.1-3B model optimized for efficient local inference using llama.cpp.

IQ3_M

• Size reduction of approx 76.18% (1.51 GB) compared to 16-bit (6.34 GB)
• Aggressive 3-bit quantization optimized for lightweight enterprise language model deployments
• Suitable for conversational AI, lightweight coding assistance, and resource-efficient inference environments
• Enables practical execution on consumer hardware while minimizing memory usage
• Complex reasoning, long-context generation, and advanced analytical tasks may exhibit reduced fidelity compared to higher-precision variants

IQ4_NL

• Size reduction of approx 70.50% (1.87 GB) compared to 16-bit (6.34 GB)
• Advanced 4-bit non-linear quantization designed to better preserve reasoning capability and instruction-following quality
• Better suited for production assistants, RAG pipelines, structured generation, and coding workflows
• Designed to reduce quantization loss while maintaining response consistency across diverse NLP tasks
• May require slightly increased computational overhead during inference

IQ4_XS

• Size reduction of approx 71.92% (1.78 GB) compared to 16-bit (6.34 GB)
• Balanced 4-bit quantization focused on efficient inference and dependable language generation
• Provides a practical balance between memory efficiency, response quality, and runtime performance
• Suitable for enterprise assistants, workflow automation, summarization, and production AI services
• Maintains reliable performance across most real-world language understanding and text-generation workloads

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Training Background (Original Model)

Granite-4.1-3B is trained with an emphasis on efficient language understanding, instruction following, reasoning, coding, and enterprise-oriented NLP tasks across diverse multilingual datasets.

Pretraining

• Large-scale language pretraining across multilingual and domain-diverse text corpora
• Focus on contextual language understanding, reasoning, and knowledge representation
• Optimized for downstream conversational AI, coding, retrieval, and text-generation workloads

Instruction Tuning

• Refined using high-quality instruction-following and dialogue datasets
• Enhanced for conversational consistency, structured response generation, and tool-oriented interactions
• Improved performance across coding, summarization, question answering, and enterprise assistant workflows

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Key Capabilities

• Instruction Following Produces reliable responses aligned with user instructions across a broad range of tasks.

• Reasoning Supports logical reasoning and multi-step analytical problem solving.

• Code Generation Assists with code completion, explanation, debugging, and programming tasks.

• Retrieval-Augmented Generation (RAG) Integrates effectively into retrieval-based AI systems and knowledge assistants.

• Structured Output Generation Generates organized responses suitable for automation, APIs, and downstream applications.

• Efficient Local Deployment Quantized variants enable practical deployment on consumer hardware and edge systems.

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Usage Example

Using llama.cpp

./llama-cli \
  -m SandLogicTechnologies/Granite-4.1-3B_IQ4_NL.gguf \
  -p "Summarize this document and extract the key action items."

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Recommended Usecases

• Conversational AI Deploy efficient local AI assistants.

• Coding Assistance Generate, explain, and debug source code.

• Enterprise AI Build knowledge assistants and internal productivity tools.

• Workflow Automation Generate structured responses for business processes and automation pipelines.

• RAG Applications Power retrieval-augmented enterprise search and question-answering systems.

• Research & Experimentation Evaluate compact language models for efficient local inference.

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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-4.1-3B model.

• Georgi Gerganov and the llama.cpp open-source community for enabling efficient quantization and inference via the GGUF format.

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Contact

For questions, feedback, or support, please reach out at [email protected] or visit https://www.sandlogic.com/