Train low-rank adapters for parameter-efficient fine-tuning of large language models.
npx skills add https://github.com/Z43L/zeus-agent --skill peft-fine-tuning-z43l
Please help me install this Agent Skill. Skill: peft-fine-tuning Source: https://github.com/Z43L/zeus-agent/tree/main/optional-skills/mlops/peft Command: npx skills add https://github.com/Z43L/zeus-agent --skill peft-fine-tuning-z43l
This Skill helps you fine-tune large language models without paying the full compute and memory cost of updating all parameters.
Use case example: You want to adapt a 7B–70B instruction model for a narrow business domain on a single GPU; use LoRA for fast experiments, switch to QLoRA when VRAM is the bottleneck, and then merge the adapter into the base model for efficient deployment.
Use the peft-fine-tuning skill to run LoRA or QLoRA fine-tuning for your target model and dataset, producing a small adapter you can load later or merge for deployment.
High-intent search queries and answers about installing and using this skill.
You can fine-tune large language models on limited VRAM by training low-rank adapters with LoRA or QLoRA. This parameter-efficient approach updates under 1% of model parameters, drastically reducing memory costs while adapting instruction models for narrow domains.
LoRA trains low-rank adapters to reduce memory during fine-tuning, while QLoRA combines this with 4-bit quantization via bitsandbytes. Switch to QLoRA when VRAM is the bottleneck for 7B-70B models, and use LoRA for faster iteration experiments.
Yes, multi-adapter workflows allow you to create and serve multiple task-specific adapters from one base model. You can switch adapters at runtime or compose them for ensemble behavior without loading multiple full models into memory.
You can merge trained adapters directly into the base model for adapter-free serving. This deployment option eliminates the overhead of loading adapters separately during inference, streamlining the production pipeline.
Parameter-efficient fine-tuning uses the PEFT, transformers, and torch stack. Optional dependencies include bitsandbytes for 4-bit quantization, datasets for data loading, accelerate for distributed training, and trl for reinforcement learning integration.
QLoRA trains adapters using 4-bit quantization via bitsandbytes, producing small adapter files. These adapters can be loaded or merged into the base model, preparing them for efficient deployment and inference using frameworks like vllm.
ovachieverOptimize LLM preference alignment without a reference model using comparison data.
RachasumanthDeploy trained machine learning models for inference using vLLM, TGI, Ollama, and llama.cpp.
jonnabioQuantize LLMs to 4-bit precision using group-wise quantization.
BermudaLocalsFine-tune large language models with LoRA and QLoRA adapters.
