R6410418/Jackrong-llm-finetuning-guide Jupyter Notebook
Jackrong LLM Fine-tuning Guide: Pedagogical Architecture & Training Efficiency
This repository implements a progressive disclosure pedagogical model for LLM fine-tuning, integrating Unsloth's optimized training kernels with unified abstractions across Llama3, Qwen, and DeepSeek architectures. The notebook-based approach systematically bridges theoretical optimization techniques (QLoRA, gradient checkpointing) with empirical memory profiling, targeting the efficiency gap between research implementations and production fine-tuning pipelines.
391 Updated 2026-04-08T16:14:52.888Z