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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.
+
+