diff --git a/llm_experiments/README.md b/llm_experiments/README.md new file mode 100644 index 000000000..d49513f9d --- /dev/null +++ b/llm_experiments/README.md @@ -0,0 +1,89 @@ + + + +# 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](#introduction) +- [Series Overview](#series-overview) +- [Getting Started](#getting-started) +- [Notebooks and Scripts](#notebooks-and-scripts) +- [Contributing](#contributing) +- [License](#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](LICENSE) file for more details. + +