Finetune_LLMs

Introduction to Finetune LLMs Project

Overview

The Finetune LLMs project provides a comprehensive solution for fine-tuning Large Language Models (LLMs) using a dataset of famous quotes. This effort is particularly beneficial for those looking to personalize or specialize language models beyond their general purpose.

The project supports several fine-tuning methods, namely DeepSpeed, Lora, and QLora. Initially, this repository was used for downloading and converting the model weights for the GPTJ model before it became available in the Huggingface transformer package. This original version is still accessible under the original_youtube branch for historical reference.

Contained within the repository are essential components like the quotes_dataset folder which houses the curated dataset tailored for fine-tuning needs. This dataset takes inspiration from a project available here. Furthermore, the finetuning_repo directory includes adapted code from another repository, expanding its functionality to accommodate more models and a range of methods. You can find the original source here.

Professional Assistance

For anyone requiring professional, paid support, assistance can be obtained through contacting the provided email.

Old Video Walkthroughs (For Historical Reference)

Originally, video tutorials were available which guide users through using the repository's initial codebase. A walkthrough for this original code is accessible here.

A more recent walkthrough, focusing on utilizing the Huggingface model, is provided here. While these may be resourceful, it is recommended to adhere to modern methods unless explicitly needed.

Updated Docker Walkthrough (Recommended for Regular Use)

For those looking to utilize the Finetune LLMs project with ease, an updated walkthrough leveraging Nvidia-docker assists in streamlining the process.

Requirements

To effectively run this project, there are several prerequisites:

1. A capable Nvidia GPU, generally with at least 24GB VRAM and support for fp16 operations. For cloud environment usage, the A100 is recommended due to its superior speed and VRAM capacity.
2. A Linux operating system, specifically Ubuntu is recommended.
3. A modern version of Docker should be installed. When uncertain, updating to the latest version is advisable.
4. Nvidia-docker must be installed to permit GPU passthrough to Docker containers. The installation guide can be found here.
5. Ensure that the latest Nvidia drivers are installed on your system. The necessary tool for this can be accessed here.

Example for Cuda Drivers

For users requiring an A100 driver on a 64-bit Linux system, a sample command can be used to facilitate setup:

wget https://us.download.nvidia.com/tesla/515.86.01/NVIDIA-Linux-x86_64-515.86.01.run

After downloading, execute it with administrative privileges:

chmod 777 NVIDIA-Linux-x86_64-515.86.01.run
sudo ./NVIDIA-Linux-x86_64-515.86.01.run

Usage Instructions

1. Build the Docker image by executing the build_image.sh script. If errors occur related to image versions, consider updating to a more recent Cuda version, as older images may have been deprecated. After resolving such issues, consider submitting a Pull Request (PR) to aid others.
2. Execute run_image.sh to initiate the Docker image build process and automatically mount the current working directory to /workspace inside the Docker container. All of the GPUs on your system will be accessible, and caching is managed by passing your local .cache into the container.
3. Now set up, this Docker container enables you to fine-tune models with GPU assistance or use DeepSpeed inference. For further detailed information, refer to the corresponding sections in the repository.

This introduction should provide a comprehensive guide to getting started with the Finetune LLMs project. The detailed instructions and resources ensure that users can adequately tailor language models to their specific tasks or research needs.