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# 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.