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# Mixtral-Experiment Series
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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.
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## Table of Contents
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- [Introduction](#introduction)
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- [Series Overview](#series-overview)
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- [Getting Started](#getting-started)
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- [Notebooks and Scripts](#notebooks-and-scripts)
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- [Contributing](#contributing)
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- [License](#license)
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## Introduction
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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.
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## Series Overview
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The Mixtral-Experiment series will cover the following topics:
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1. **Understanding LLM Architectures**:
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- An overview of popular LLM architectures like Transformers, BERT, and Mixtral.
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- Detailed explanations of key components such as embedding layers, self-attention mechanisms, and Mixture of Experts (MoE) layers.
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2. **Investigating Input Processing**:
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- How inputs are tokenized and embedded.
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- The role of attention mechanisms in processing sequences.
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- Visualizing and analyzing the outputs at various layers of the model.
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3. **Tweaking LLM Architectures**:
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- Experimenting with different configurations and hyperparameters.
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- Modifying existing LLM architectures to improve performance or adapt to specific tasks.
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- Implementing custom layers and components.
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4. **Conducting New Experiments**:
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- Designing and implementing new experiments to test hypotheses about LLM behavior.
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- Evaluating the impact of architectural changes on model performance.
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- Sharing insights and findings with the community.
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## Getting Started
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To get started with the LLM-Experiment series, you will need the following:
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1. **Python Environment**:
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- 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
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2. **Hugging Face Account**:
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- Create a Hugging Face account and obtain an API token.
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- Login to Hugging Face using the provided token or username and token.
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- Most of the Mistral,Llama models needs some sort of Agreement acceptance
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3. **Notebooks and Scripts**:
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- Clone this repository to access the notebooks and scripts or you can directly open in Google Colab
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- Follow the instructions in each notebook to run the experiments and analyze the results.
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## Notebooks and Scripts
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The series will include the following notebooks and scripts:
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1. **Mixtral Model Analysis**:
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- Analyzing the architecture and configuration of the Mixtral model.
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- Registering hooks to capture the outputs at various layers.
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2. **Input Processing and Embedding**: - Upcoming
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3. **Attention Mechanisms and improvements**: - Upcoming
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4. **Rolling Buffer,KV-cache,Sliding Window Attention**: - Upcoming
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5. **Tweaking Model Architectures - Adapters,Down-Casting**: - Upcoming
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## Contributing
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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.
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## License
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This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
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