peft_skill

Overview

This skill provides parameter-efficient fine-tuning (PEFT) for large language models using LoRA, QLoRA, and 25+ adapter methods. It enables training <1% of parameters so you can fine-tune 7B–70B models on consumer or limited-memory GPUs and serve multiple small adapters from a single base model. The implementation integrates with the Transformers ecosystem for training, quantization, and multi-adapter serving.

How this skill works

The skill wraps a base transformer model with lightweight adapter modules (LoRA, IA3, Prefix, etc.) and trains only those adapter parameters while keeping the base weights frozen. For extreme memory constraints it combines k-bit quantization (QLoRA) and adapter training to fit 70B models on a single 24GB GPU. It also supports saving, loading, merging, and runtime switching of adapters for multi-task or multi-tenant serving.

When to use it

• Fine-tuning 7B–70B models on consumer GPUs (RTX 4090, A100)
• You need to train <1% of parameters with minimal storage footprint
• Iterating quickly with multiple task-specific adapters
• Deploying multiple fine-tuned variants from one shared base model
• Memory-limited scenarios where QLoRA lets you train very large models on a single 24GB GPU

Best practices

• Start with LoRA rank r=8–16 and alpha ≈ 2*r; raise r if quality is insufficient
• Target attention and MLP linear layers (or use automatic all-linear detection)
• Enable gradient checkpointing and use QLoRA for 70B+ models to reduce memory
• Save adapter checkpoints frequently; adapters are small and make rollback simple
• Evaluate adapters on held-out data before merging into the base model
• Use higher ranks (32–64) for complex domain adaptation or 70B-scale models

Example use cases

• Fine-tune a 8B Llama model for a specific knowledge domain on a single A100 with LoRA r=16
• Train a 70B model on a 24GB GPU using QLoRA + LoRA to produce a 6–50MB adapter
• Serve multiple business-specific response styles by loading adapters at runtime and switching per request
• Create low-cost, iteratively improved task adapters for chatbot instruction tuning and merge the best into a deployed model
• Use IA3 or prompt-prefix tuning for ultra-low-parameter few-shot adaptation or personalization