LLM Fine-Tuning Large Language Models Project Overview
The LLM Fine-Tuning Large Language Models project, created and curated by Rohan Paul, is a comprehensive resource for those interested in the art and science of fine-tuning large language models (LLMs). This project is accompanied by detailed explanations and educational content available on YouTube, making it accessible to both beginners and advanced users.
Fine-Tuning LLM with Tutorials and Videos
One of the project's highlights is its collection of Jupyter Notebooks, each paired with corresponding YouTube videos. These resources provide step-by-step guidance on fine-tuning various LLMs using different techniques and datasets. Some examples of the fine-tuning efforts include:
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Llama-3 Models: Fine-tuning Llama-3 models using the Unsloth 4bit quantization technique and the ORPO method. These notebooks provide insights into handling large datasets and custom dataset fine-tuning.
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Conversational Agents: Building conversational agents using the CodeLLaMA-34B model, enhanced with Streamlit for creating interactive web applications.
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Quiz Applications: Developing applications like the Yarn-Llama-2-13b-128k, which uses a KV Cache to answer quizzes based on lengthy textbooks.
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Mistral 7B Fine-Tuning: This section explores fine-tuning methods using PEFT (Parameter Efficient Fine Tuning) and QLORA approaches. These techniques optimize model performance while maintaining parameter efficiency.
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Web Scraping with LLMs: Integrating LLMs with web scraping technologies using frameworks like AnthropicAI and LangChainAI to extract valuable data from the web.
Detailed Notebooks and Practical Techniques
The project offers a variety of detailed notebooks that delve into fine-tuning and optimization methods for LLMs:
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ORPO and Precision Techniques: Notebooks demonstrate fine-tuning processes such as the Gemma_2b and Jamba models using ORPO (Over-Regularized Pretrain and Operate) and full precision techniques on platforms like Google Colab.
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Chatbot Development: Guides to creating chatbots with Mixtral and Gradio, and utilizing APIs like TogetherAI for enhanced language model interactions.
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Dataset-Specific Fine-Tuning: Detailed instructions on how to finetune models like the Mistral 7B using the finance-alpaca dataset, or adapting the TinyLlama for specific data like song lyrics.
LLM Techniques and Utilities
In addition to fine-tuning, the project provides educational content on LLM techniques and utilities. This includes:
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Quantization Techniques: Guidance on methods like 4-bit LLM quantization using GPTQ to ensure efficient model performance without compromising accuracy.
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Embedding and Optimization: Explaining the role of rotary embeddings in LLMs, optimization strategies like Direct Preference Optimization (DPO), and nuances in matrix math related to LoRA (Low-Rank Adaptation).
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Performance and Evaluation: Instructions on evaluating LLMs, checking token processing rates, and understanding validation log perplexity to enhance the training process results.
Smaller Language Models and Additional Resources
Besides large LLMs, the project touches upon smaller language models such as DeBERTa and BERT for various tasks, from sentiment classification and multi-class classification to named entity recognition. Each comes with practical tutorials and accompanying video resources.
Whether one is delving into large-scale language models or exploring smaller model applications, the LLM Fine-Tuning project offers a wealth of knowledge and practical insights to further AI capabilities in natural language processing. For more information, you can check out Rohan Paul's content on YouTube, Twitter, LinkedIn, and Kaggle.