Train LoRA/QLoRA adapters for parameter-efficient fine-tuning of large language models.
npx skills add https://github.com/devMoez/titan --skill peft-fine-tuning-devmoez
Please help me install this Agent Skill. Skill: peft-fine-tuning Source: https://github.com/devMoez/titan/tree/main/optional-skills/mlops/peft Command: npx skills add https://github.com/devMoez/titan --skill peft-fine-tuning-devmoez
Parameter-efficient fine-tuning (PEFT) solves the problem of adapting large language models without the cost and GPU memory required for full fine-tuning.
Use case example: You want to adapt a 70B instruction-tuned model to your domain on a single 24GB GPU; you use QLoRA to train adapters that fit locally, then deploy either as adapters (multi-variant serving) or by merging into a standalone model.
Use the PEFT/LoRA approach to fine-tune a 7B model on your instruction dataset while training only small low-rank adapter weights.
High-intent search queries and answers about installing and using this skill.
You can fine-tune large models on limited hardware using parameter-efficient fine-tuning. By training small LoRA adapters while keeping the base model frozen, or using QLoRA with 4-bit quantization, you can fit significantly larger models into constrained GPU memory.
LoRA trains small low-rank adapter weights while keeping the base model frozen, whereas QLoRA adds 4-bit quantization on top of LoRA. QLoRA reduces memory requirements further, allowing you to fit much larger models on consumer hardware like a single 24GB GPU.
PEFT supports multi-adapter workflows by allowing you to create multiple task-specific adapters. You can switch between these adapters at inference time or serve them in combination to handle different tasks and variants efficiently.
For deployment after training, you can save the lightweight adapters and either serve them as multi-variant adapters or merge them directly into the base model. Merging adapters creates a standalone model for simpler deployment and faster inference.
Yes, you can use bitsandbytes for 4-bit quantization during parameter-efficient fine-tuning. This integration enables QLoRA, which drastically reduces GPU memory usage so you can train large language models on consumer hardware.
Parameter-efficient fine-tuning requires HuggingFace PEFT integrated with transformers, plus compatible versions of torch. Optionally, bitsandbytes is required for QLoRA quantization to further reduce memory consumption on consumer GPUs.
ever-oliFine-tune HuggingFace Transformers with TRL for RLHF alignment.
thinking-machines-labDownloads, merges LoRA adapters, and publishes models to HuggingFace Hub.
ricableQuantize Hugging Face Transformers models with bitsandbytes for 8-bit and 4-bit inference.
SignmanalTrains less than 1% of LLM weights using LoRA, QLoRA, and 25+ PEFT methods with HuggingFace Transformers, TRL, and vLLM integration.
