Welcome to Our LLM Research Implementation Project!

[parthasarathydNU]

Welcome to Our LLM Research Implementation Project!

Hello everyone, and welcome to our collaborative space for implementing cutting-edge Large Language Model research! 👋

Our Vision

This repository is more than just a collection of code—it's a learning community. Our goal is to make advanced AI concepts accessible to everyone, from seasoned ML engineers to curious newcomers. We believe that the best way to understand complex ideas is by implementing them, discussing them, and learning together.

Recently Implemented

• Direct Preference Optimization for LLM Fine-tuning : Implementing the technique described in the "Direct Preference Optimization" paper to fine-tune large language models (LLMs) to better understand and align with human preferences
• Implementation of Instruction Backtranslation Paper: Instruction backtranslation is revolutionizing the way LLMs are aligned to follow instructions. By leveraging vast amounts of unlabelled data and minimal human-annotated seed data, this technique generates high-quality instructional data through an iterative self-training process
• Generative Pretained Transformer: Developed a GPT transformer model from scratch, implementing the MultiHead Attention mechanism and training it for text generation tasks

How You Can Contribute

There are many ways to be part of this project, regardless of your experience level:

1. Implement a Paper: Choose a recent LLM paper and try to code it up.
2. Improve Existing Code: See something that could be optimized or clarified? Go for it!
3. Documentation: Help explain complex concepts in simple terms.
4. Testing and Benchmarking: Run experiments and share your results.
5. Suggest New Directions: Have ideas for new papers or techniques we should explore? Let us know!
6. Start a Discussion: Read through an implementation and share what you understood or what confused you. Your questions and insights can help everyone learn.

For First-Time Contributors

Feeling a bit overwhelmed? Don't worry—we all start somewhere! Here are some ways to ease in:

• Introduce Yourself: Tell us about your background and what interests you about LLMs.
• Ask Questions: No question is too basic. If you're wondering about something, others probably are too.
• Share Resources: Found a helpful article, video, or tutorial? Post it here!
• Offer Feedback: Your fresh perspective is valuable. If something isn't clear, let us know.
• Available Issues : Check out current issues that members are working on to get an idea of the work that we do here and feel free to hop on to any issue or create an issue and start working at it

Shaping This Repository

This is a community project, and your input matters. We want to hear your ideas on:

• What papers or techniques should we tackle next?
• How can we make our implementations more accessible or efficient?
• What additional resources would be helpful for learners?
• How can we improve our documentation or code structure?

Let's Get Started!

Use this thread to introduce yourself, share your thoughts, or ask your first questions. Remember, we're all here to learn and grow together.

Looking forward to collaborating with all of you on this exciting journey into the world of LLMs!

#AILearning #Collaboration #OpenSource

[parthasarathydNU]

Tasks I am currently working on can be found in the issues that I have listed. Feel free to let me know if you want to hop on aboard !

[Gaurav-Krishna-Gaali]

Looks intresting. I'm gonna go through this stuff.

[parthasarathydNU]

Feel free to ask any questions / reach out for clarifications. Love to hear your thoughts.