A tutorial resource providing structured learning materials and practical examples for large-language-model engineering, training, and application delivery. It shares technical principles alongside parameter-efficient fine-tuning guides and accompanying code links for several examples.
Project overview
Combines LLM training tutorials spanning multiple methodologies with links to accompanying code for several examples.
Project type
Model Development · Prompt Engineering · Learning Resources
Use cases
Learning & Education
Deployment
Refer to project documentation
License
Apache-2.0
Best for
Developers and researchers seeking structured learning materials and practical examples for full fine-tuning, parameter-efficient fine-tuning, and RLHF.
Key capabilities
Provides a curated set of practical LLM training tutorials spanning full fine-tuning, parameter-efficient fine-tuning, and RLHF.
Provides practical guides for parameter-efficient fine-tuning techniques supported by HuggingFace PEFT, with accompanying code for several tutorials.
Limitations and risks
The distributed training network communication section is marked as awaiting updates.
Operating system requirements, GPU requirements, minimum hardware specifications, database requirements, external service dependencies, and model requirements are currently unknown.
Getting started
Installation difficulty, operating system requirements, minimum hardware, database requirements, and model requirements are not documented.