PEFT (Parameter-Efficient Fine-Tuning)

Kheish Compatibility

This skill is repo-local and stays inactive until explicitly activated.

When the original instructions refer to legacy tool names, use these Kheish mappings:

• terminal => bash
• web_extract => web_fetch, plus web_search when discovery is needed
• search_files => grep_search and glob_search
• browser_* tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly

When the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.

PEFT (Parameter-Efficient Fine-Tuning)

Fine-tune LLMs by training <1% of parameters using LoRA, QLoRA, and 25+ adapter methods.

When to use PEFT

Use PEFT/LoRA when:

• Fine-tuning 7B-70B models on consumer GPUs (RTX 4090, A100)
• Need to train <1% parameters (6MB adapters vs 14GB full model)
• Want fast iteration with multiple task-specific adapters
• Deploying multiple fine-tuned variants from one base model

Use QLoRA (PEFT + quantization) when:

• Fine-tuning 70B models on single 24GB GPU
• Memory is the primary constraint
• Can accept ~5% quality trade-off vs full fine-tuning

Use full fine-tuning instead when:

• Training small models (<1B parameters)
• Need maximum quality and have compute budget
• Significant domain shift requires updating all weights

Quick start

Installation