LLM Fine-Tuning from Scratch
“Master the complete fine-tuning pipeline from basics to covering tokenization, dataset curation, LoRA & QLoRA techniques, training on Hugging Face, evaluating model performance, and deploying your custom LLM via API.”
Module 1: Foundations of LLMs & Tokenization
By the end of this module you will be able to explain the core components of Transformer models, understand various tokenization strategies, and implement custom tokenizers using the Hugging Face `tokenizers` library.
Module 2: Dataset Curation & Preparation
By the end of this module you will be able to identify suitable datasets for fine-tuning, apply data cleaning and preprocessing techniques, and format datasets effectively for various LLM fine-tuning tasks using the Hugging Face `datasets` library.
Module 3: Introduction to Fine-Tuning & PEFT
By the end of this module you will be able to differentiate between various fine-tuning strategies, understand the concept and benefits of Parameter-Efficient Fine-Tuning (PEFT), and implement LoRA for fine-tuning a pre-trained LLM on a specific task.
Module 4: Advanced PEFT: QLoRA & Training with Hugging Face
By the end of this module you will be able to understand and apply QLoRA for highly memory-efficient fine-tuning, set up a complete training pipeline using Hugging Face `Trainer`, and manage training parameters effectively.
Module 5: Model Evaluation & Benchmarking
By the end of this module you will be able to select appropriate metrics for evaluating generative LLMs, implement evaluation pipelines, and interpret model performance against common benchmarks.
Module 6: Deployment & API Integration
By the end of this module you will be able to save and load fine-tuned models, explore various deployment options for LLMs, and build a basic API to serve your custom LLM for inference.
Learn
Watch curated videos and read study resources
Practice
Practice what you learned
Build Projects
Build projects using your new gained knowledge
Submit & Verify
Submit your project and get verified by our system
References
Rate this roadmap
Help the community find verified technical paths.
Community Insights
0Join the discussion
Sign in to share your thoughts and technical insights.
Loading insights...