Turbocharge LLaMA Fine-Tuning with Tuna-Asyncio: A No-Code Solution

Introduction

I was very excited to create own AI model. That I can set my information data. and AI will understand me. What i am trying to think or do. It will be like best powerfull assistant of the world. SO, I start research about it. and I figureout createing own custom dataset is the main importent part, to train ai or you can call it Fine-Tuning. Now, the questions come out how the dataset would look like . very simple. just one line of question and one line of answer. creating this kind of dataset createing question from a large data is a very big challenge. Therefore let me intridue Tuna-asyncio solution.

But first let me summer up : Fine-tuning large language models (LLMs) like LLaMA can be a complex and resource-intensive process. However, with the introduction of Tuna-Asyncio with LLaMA, generating synthetic fine-tuning datasets has never been easier. This no-code tool enables anyone, regardless of technical expertise, to create high-quality training data for LLaMA models.

What is Tuna-Asyncio with LLaMA?

1. Prepare Your Data

Tuna-Asyncio with LLaMA is a Python-based tool. You have to input chunk.csv where will be a chunk of data of each line. it will send it to local llama. And appending to output.csv what will append ? Question and answer (DATASET)

2. Generate Prompt-Completion Pairs

After preparing your data, run the main.py script. This script processes the chunk.csv file and generates a JSON file, output_alpaca.json, in the Alpaca format. This file will contain the prompt-completion pairs needed for fine-tuning your LLaMA model.

How to Use Tuna-Asyncio Dataset to Fine-Tuning LLaMA.

So, Great your dataset is reddy. Now lets talk about using this dataset to Fine-Tuning LLAMA . Fist question Do you have a powerful GPU with mimimum of 16gb GPU VRAM ? if you dont have you should use google colab. because if offer a free limited powerful GPU.

https://gitlab.com/krafi/tuna-asyncio-with-llama

3. Fine-Tuning on Google Colab

  1. Open the Google Colab link.

  2. Upload your output_alpaca.json file to the LLaMA-Factory/data directory in the Colab file manager.

  3. Modify the identity.json file in the same directory to include the path to your output_alpaca.json file:

    {
  "identity": {
    "file_name": "identity.json"
  },
  "alpaca_en_demo": {
    "file_name": "alpaca_en_demo.json"
  },
  "output_alpaca.json": {
    "file_name": "output_alpaca.json"
  },
  "alpaca_zh_demo": {
    "file_name": "alpaca_zh_demo.json"
  }
  // ... other configurations
}

  4. Continue running the remaining cells in the notebook to complete the fine-tuning process. You can skip the “Fine-tune model via LLaMA Board” section (if you don’t need a web interface.)

Benefits of Using Tuna-Asyncio with LLaMA

  • Speed and Efficiency: Quickly generate large volumes of training data with minimal effort.
  • User-Friendly: Ideal for users with limited technical expertise.
  • Customizable: Fine-tune LLaMA models on datasets tailored to your specific needs.

Conclusion

Tuna-Asyncio with LLaMA is a game-changer for anyone looking to fine-tune LLaMA models. This tool simplifies the process of creating high-quality, synthetic fine-tuning datasets, making it accessible to a broader audience. Whether you’re an AI researcher or a developer, Tuna-Asyncio with LLaMA will help you take your LLaMA models to the next level.

https://gitlab.com/krafi/tuna-asyncio-with-llama

Turbocharge LLaMA Fine-Tuning with Tuna-Asyncio: A No-Code Solution