Python/llm_experiments/README.md

Mixtral-Experiment Series

Welcome to the Mixtral-Experiment series! This series of notebooks and scripts aims to provide a comprehensive guide on investigating the internal workings of Large Language Models (LLMs), understanding how they process inputs, and experimenting with their architectures.

Table of Contents

• Introduction
• Series Overview
• Getting Started
• Notebooks and Scripts
• Contributing
• License

Introduction

Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP) by achieving state-of-the-art performance on various tasks. However, understanding their internal workings and how they process inputs can be challenging. This series aims to demystify LLMs by providing detailed explanations, hands-on experiments, and practical tips for tweaking their architectures.

Series Overview

The Mixtral-Experiment series will cover the following topics:

1. Understanding LLM Architectures:

   • An overview of popular LLM architectures like Transformers, BERT, and Mixtral.
   • Detailed explanations of key components such as embedding layers, self-attention mechanisms, and Mixture of Experts (MoE) layers.

2. Investigating Input Processing:

   • How inputs are tokenized and embedded.
   • The role of attention mechanisms in processing sequences.
   • Visualizing and analyzing the outputs at various layers of the model.

3. Tweaking LLM Architectures:

   • Experimenting with different configurations and hyperparameters.
   • Modifying existing LLM architectures to improve performance or adapt to specific tasks.
   • Implementing custom layers and components.

4. Conducting New Experiments:

   • Designing and implementing new experiments to test hypotheses about LLM behavior.
   • Evaluating the impact of architectural changes on model performance.
   • Sharing insights and findings with the community.

Getting Started

To get started with the LLM-Experiment series, you will need the following:

1. Python Environment:

   • All these notebooks are created in Kaggle or Google Colab, So it's recommended to use the same to reproduce the results for other models

2. Hugging Face Account:

   • Create a Hugging Face account and obtain an API token.
   • Login to Hugging Face using the provided token or username and token.
   • Most of the Mistral,Llama models needs some sort of Agreement acceptance

3. Notebooks and Scripts:

   • Clone this repository to access the notebooks and scripts or you can directly open in Google Colab
   • Follow the instructions in each notebook to run the experiments and analyze the results.

Notebooks and Scripts

The series will include the following notebooks and scripts:

1. Mixtral Model Analysis:

   • Analyzing the architecture and configuration of the Mixtral model.
   • Registering hooks to capture the outputs at various layers.

2. Input Processing and Embedding: - Upcoming

3. Attention Mechanisms and improvements: - Upcoming

4. Rolling Buffer,KV-cache,Sliding Window Attention: - Upcoming

5. Tweaking Model Architectures - Adapters,Down-Casting: - Upcoming

Contributing

We welcome contributions from the community! If you have any ideas, suggestions, or improvements, please feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.