Granite Company Agent

Diagram illustrating the Granite Company Agent workflow.

Overview

Key features:

• LoRA-based parameter-efficient fine-tuning.
• Easy dataset generation and preprocessing.
• Simple inference script to chat with the model.
• Built with Hugging Face Transformers, PEFT, and PyTorch.

Repository Structure

granite-company-agent/
│
├── README.md
├── LICENSE
├── requirements.txt
│
├── train_granite.py       # Script to fine-tune the model
├── data_loader.py         # Utility to load CSV, XLSX, or TXT files
├── generate_data.py       # Generate synthetic company data
├── download_model.py      # Download base model from Hugging Face
├── chat_agent.py          # Simple inference/chat script

Setup

Clone the repository:

git https://huggingface.co/MateusBarros/granite-company-agent.git
cd granite-company-agent

Install dependencies:

pip install -r requirements.txt

python generate_data.py

Download the base model:

Training

Training configuration highlights:

• Epochs: 3
• Batch size: 2 (gradient accumulation used)
• Learning rate: 2e-4
• LoRA fine-tuning (low-rank adapters)
• BF16 precision The trained model and tokenizer are saved in ./granite_company_agent.

Inference / Chat

License

References

• Hugging Face Transformers
• PEFT: Parameter-Efficient Fine-Tuning
• PyTorch