Granite-4.0-Micro

Summary

• Introduction
• Parameters
• Usage for developers
• Reference
• License
• Citation

Introduction

Granite-4.0-Micro is a 3B parameter long-context instruct model finetuned from Granite-4.0-Micro-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. Granite 4.0 instruct models feature improved instruction following (IF) and tool-calling capabilities, making them more effective in enterprise applications.

• Developers: Granite Team, IBM
• HF Collection: Granite 4.0 Language Models HF Collection
• GitHub Repository: ibm-granite/granite-4.0-language-models
• Website: Granite Docs
• Release Date: October 2nd, 2025
• License: Apache 2.0

Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.0 models for languages beyond these languages.

Intended use: The model is designed to follow general instructions and can serve as the foundation for AI assistants across diverse domains, including business applications, as well as for LLM agents equipped with tool-use capabilities.

Capabilities

• Summarization
• Text classification
• Text extraction
• Question-answering
• Retrieval Augmented Generation (RAG)
• Code related tasks
• Function-calling tasks
• Multilingual dialog use cases
• Fill-In-the-Middle (FIM) code completions

Generation: This is a simple example of how to use Granite-4.0-Micro model.

Parameters

Inputs

• prompt - (text): User-provided input can be a question, a request, or the context to be addressed. The system uses the prompt to analyze, infer, and generate a corresponding response.

Output

• output - (text): The content generated after the token is the final answer that the model returns to the user.

Usage for developers

Please find below the details to track the information and access the code for processing the model on our platform.

Requirements

pip install -r requirements.txt

Code based on AIOZ structure

import os
from transformers import AutoModelForCausalLM, AutoTokenizer

...

def do_ai_task(
        prompt: Union[str, str],
        model_storage_directory: Union[str, Path],
        device: Literal["cpu", "cuda", "gpu"] = "cpu",
        *args, **kwargs) -> Any:
    """Define AI task: load model, pre-process, post-process, etc ..."""
    # Define AI task workflow. Below is an example
    model_path = os.path.join(model_storage_directory, "/granite-4.0-micro")

    tokenizer = AutoTokenizer.from_pretrained(model_path)
    # drop device_map if running on CPU
    model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
    model.eval()

    ...
    
    output = tokenizer.batch_decode(output)

    return output[0]

Reference

This repository is based on and inspired by IBM Granite's work. We sincerely appreciate their generosity in sharing the code.

License

We respect and comply with the terms of the author's license cited in the Reference section.

Citation

@misc{granite2025,
  author       = {{IBM Research}},
  title        = {Granite 4.0 Language Models},
  year         = {2025},
  howpublished = {\url{https://github.com/ibm-granite/granite-4.0-language-models}},
  note         = {Accessed: 2025-10-01}
}