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RT Finetuning Scripts
⚠️Clear Notebook Before Use
This repository contains the training and fine-tuning scripts for the following models and adapters:
- Llama
- Qwen
- SmolLM
- DeepSeek
- Other Adapters
Overview
These scripts are designed to help you fine-tune various language models and adapters, making it easy to train or adapt models to new datasets and tasks. Whether you want to improve a model’s performance or specialize it for a specific domain, these scripts will facilitate the process.
Features
- Training Scripts: Easily train models on your own dataset.
- Fine-Tuning Scripts: Fine-tune pre-trained models with minimal setup.
- Support for Multiple Models: The scripts support a variety of models including Llama, Qwen, SmolLM, and DeepSeek.
- Adapter Support: Fine-tune adapters for flexible deployment and specialization.
Requirements
Before running the scripts, make sure you have the following dependencies:
- Python 3.x
transformerslibrarytorch(CUDA for GPU acceleration)- Additional dependencies (see
requirements.txt)
Installation
Clone the repository and install dependencies:
git clone https://github.com/your-repo/rt-finetuning-scripts.git
cd rt-finetuning-scripts
pip install -r requirements.txt
Usage
Fine-Tuning a Model
- Choose a model: Select from Llama, Qwen, SmolLM, or DeepSeek.
- Prepare your dataset: Ensure your dataset is formatted correctly for fine-tuning.
- Run the fine-tuning script: Execute the script for your chosen model.
Contributing
Contributions are welcome! If you have improvements or bug fixes, feel free to submit a pull request.
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