Fine-tune large language models with LoRA and QLoRA adapters.
npx skills add https://github.com/ricable/mcai --skill peft-fine-tuning-ricable
Please help me install this Agent Skill. Skill: peft-fine-tuning Source: https://github.com/ricable/mcai/tree/main/.agents/skills/peft-fine-tuning Command: npx skills add https://github.com/ricable/mcai --skill peft-fine-tuning-ricable
This Skill helps you fine-tune large language models without the cost of full-model training, making adaptation practical on limited GPU budgets while preserving strong task performance.
Use the peft-fine-tuning skill to recommend the best PEFT method for my model, GPU memory, dataset type, and deployment target.
High-intent search queries and answers about installing and using this skill.
Fine-tune a 70B model on a single 24GB GPU by applying QLoRA, which quantizes the base model and trains parameter-efficient adapters to minimize memory overhead while preserving task performance.
LoRA, QLoRA, DoRA, and AdaLoRA are parameter-efficient fine-tuning variants offering different memory and quality tradeoffs, with QLoRA adding quantization for lower memory limits and DoRA modifying weight updates.
Configure PEFT adapters for instruction tuning and domain adaptation by setting the appropriate rank, target modules, and adapter method within Hugging Face workflows to update only specific layers efficiently.
Yes, multi-adapter serving allows you to deploy multiple LoRA adapters simultaneously with vLLM, enabling dynamic switching between customized models without loading duplicate base weights into memory.
PEFT training fails with out-of-memory errors when GPU limits are exceeded, which you can troubleshoot by switching to QLoRA, reducing batch size, or adjusting adapter rank and target modules.
Merge trained adapters back into the base model using supported PEFT configurations, which combine the adapted weights with the original model for standalone deployment without loading adapter files separately.
SethyPagnaFine-tune large language models with parameter-efficient adapters on memory-constrained GPUs.
Simon-Copilot-StudioFine-tune large language models with LoRA and QLoRA adapters.
moltis-orgFine-tune language models with TRL using SFT, DPO, PPO, and GRPO workflows.
GitnappConfigure LoRA and QLoRA adapters for memory-efficient LLM fine-tuning.
