This repository serves as a comprehensive learning resource for advanced Natural Language Processing (NLP) and Large Language Model (LLM) concepts, targeting engineers and researchers seeking to master modern NLP techniques. It provides a structured roadmap with associated code and video explanations for topics ranging from foundational transformer architectures to practical LLM deployment and application building.

How It Works

The project curates a series of tutorials covering key NLP advancements, including transformer architectures from scratch, BERT for text classification and NER, T5 for diverse NLP tasks, and various fine-tuning techniques like LoRA and DPO for models such as Llama2 and Mistral. It also delves into practical applications like Retrieval-Augmented Generation (RAG), model merging, and building AI-powered applications such as chatbots and coding assistants, often demonstrating deployment via Ollama.

Quick Start & Requirements

• Install/Run: Code examples are provided via GitHub links within the table. Specific installation instructions depend on individual code repositories.
• Prerequisites: Python, deep learning frameworks (e.g., PyTorch, TensorFlow), and potentially CUDA-enabled GPUs for fine-tuning and inference.
• Resources: Fine-tuning LLMs typically requires significant GPU memory and compute power.

Highlighted Details

• Covers foundational NLP models like BERT and T5.
• Explains and implements advanced fine-tuning methods (LoRA, DPO).
• Demonstrates practical LLM applications: RAG, chatbots, code assistants.
• Includes deployment strategies using Ollama and real-time agent building.

Maintenance & Community

No specific information on maintainers, community channels, or roadmap is provided in the README.

Licensing & Compatibility

The repository itself does not specify a license. Individual code repositories linked within the table would need to be checked for their respective licenses and compatibility.

Limitations & Caveats

This repository is a curated list of resources rather than a single, runnable project. Users must navigate to individual code links for setup and execution, and the lack of a unified environment or explicit licensing for the collection may pose integration challenges.