LLM Toolkit for RAG, Fine-tuning, and AI Agents
Lexo is a comprehensive collection of Jupyter notebooks designed for learning and applying Large Language Models (LLMs) in real-world scenarios. Master RAG systems, fine-tuning, AI agents, multimodal processing, and ML benchmarks through hands-on projects.
This comprehensive toolkit is the result of my certification from Ed Donner's LLM Engineering Master Course
Complete ML pipeline from data to deployment with comprehensive benchmarking and evaluation. GPU required for optimal performance.
Aggregate, clean, analyze, and balance datasets for price prediction tasks.
Compare traditional ML models against frontier LLMs for performance benchmarking.
Test contextual embeddings and retrieval-augmented generation approaches.
Fine-tune frontier models and compare before/after performance.
Evaluate quantized LLaMA 3.1 8B model performance.
Fine-tune LLaMA 3.1 using QLoRA with hyperparameter optimization.
Comprehensive evaluation and performance comparison across all models.
Final rankings and insights across ML, embeddings, RAG, and fine-tuned models.
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