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Large Language Models (LLMs)

Welcome to the Large Language Models section of the AI Engineering Academy. This module provides a comprehensive understanding of LLMs and their practical applications in AI engineering.

Repository Structure

Category Topic Resource
Introduction Overview Introduction to LLMs
Theory Behind Fine-tuning Pre-Training Pre-Training
Supervised Fine-Tuning (SFT) SFT Theory
Proximal Policy Optimization (PPO) PPO Theory
Direct Preference Optimization (DPO) DPO Theory
Observation-Regularized Policy Optimization (ORPO) ORPO Theory
Gated Regularized Policy Optimization (GRPO) GRPO Theory
Hands-On SFT Overview SFT Implementation Guide
Implementation SFT Notebook
Hands-On GRPO Guide Hacker Guide to GRPO
Implementation Qwen 0.5B GRPO
Gemma Overview Gemma Guide
Implementation Gemma Fine-tuning
Llama2 Overview Llama2 Guide
Implementation Llama2 Fine-tuning
Advanced QLora Fine-tuning
Llama3 Implementation Llama3 Fine-tuning
Mistral-7B Overview Mistral Guide
Implementation Mistral Fine-tuning
Evaluation Evaluation Harness
DPO DPO Fine-tuning
SFT SFT Trainer
Inference ChatML Inference
Mixtral Implementation Mixtral Fine-tuning
Visual Language Models Florence2 Florence2 Fine-tuning
PaliGemma PaliGemma Fine-tuning
Architecture Parameter Analysis Parameter Count

Learning Roadmap

Level Steps Resources
Beginner 1. Introduction to LLMs Introduction
2. Understanding core theory Pre-Training, SFT Theory
3. First implementation SFT Guide
4. Practical application Llama2 Fine-tuning
Intermediate 1. Advanced techniques DPO Theory, PPO Theory
2. Model implementation Mistral Fine-tuning
3. Architecture concepts Parameter Count
Advanced 1. Cutting-edge methods ORPO Theory, GRPO Theory
2. Advanced implementation GRPO Implementation
3. Multimodal models Florence2, PaliGemma

Contributing

We welcome contributions to expand this repository. Please follow the standard pull request process and ensure your contributions align with the overall structure.

License

This project is licensed under the MIT License - see the LICENSE file for details.


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