llm-fine-tuning
Fine-tune large language models with parameter-efficient methods. Covers full fine-tuning, LoRA, QLoRA, PEFT, adapter-based tuning, prefix tuning, prompt tuning, instruction tuning, RLHF (PPO, DPO), SFT (supervised fine-tuning), HuggingFace TRL and PEFT libraries, Unsloth, Axolotl, dataset preparation (Alpaca, ShareGPT, chat formats), training data quality, evaluation during fine-tuning, hyperparameter selection, memory optimization, multi-GPU fine-tuning, merging adapters, and deploying fine-tuned models. Use when fine-tuning LLMs, creating custom models, or implementing RLHF/DPO alignment.
- Version
- 1.0
- License
- Apache-2.0
Pinned to revision 45cf0fa3c5e7, so it is the text this page describes rather than whatever the author pushed since.
Files
- skills/llm-fine-tuning/SKILL.md
- skills/llm-fine-tuning/references/REFERENCE.md
- skills/llm-fine-tuning/scripts/finetune_lora.py
- skills/llm-fine-tuning/scripts/prepare_dataset.py
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