LLaMA Deployment & Training Playground

1. This repository contains the presentation slides, scripts, and documentation from a training session held on September 7, 2023.
2. The session was aimed at the Information Development Department of ChainSea and strategic partners at Anhui Yitongtianxia.
3. The material is designed for a two-hour training session focused on the LLaMA-2 model training and a demonstration of fine-tuning the LLaMA-2 7B model using the corporate data from Anhui Yitongtianxia.

Training Session Overview

Topic:

LLaMA-2 Model Training and Fine-tuning Demonstration with LLaMA-2 7B Model

Date:

September 7, 2023 (Thursday) Afternoon

Audience:

Colleagues from ChainSea Information Development Department and technical peers from Anhui Yitongtianxia.

Objective:

To provide a practical overview and hands-on experience with large language models, specifically focusing on the LLaMA-2 model's training and fine-tuning processes. Participants will learn about the underlying techniques, best practices, and detailed steps to tailor the LLaMA-2 7B model to specific organizational needs using Anhui Yitongtianxia corporate information datasets.

Repository Contents

• Deployment:

  • Scripts and configuration files for deploying the LLaMA-2 model in Linux environments.
  • Instructions for setting up the model serving infrastructure, including containerization with Docker.

• Training:

  • Step-by-step tutorials on how to initiate LLaMA-2 model training, including parameter adjustments and monitoring.
  • General example datasets and training scripts that can be adapted to any corporate data.

• PPT & PDF:

  • Presentation slides from the training session in both PowerPoint and PDF formats.
  • Additional reading materials and references for further study on LLaMA-2 model training and fine-tuning.

Prerequisites

Before beginning with the training, please ensure you have the following:

• Basic understanding of machine learning and natural language processing concepts.
• Familiarity with Python programming and scripting.
• Access to a computing environment capable of handling large language models, preferably with GPU support.

Contact Information

If you have any questions or would like to form a study group to solve problems together, please send an email to [email protected].