peft-fine-tuning Skill
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem. Published by NousResearch in hermes-agent.
What is peft-fine-tuning Skill?
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem. Published by NousResearch in hermes-agent. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Developers using hermes-agent
Technical details
- Install or run with Copy skill directory
When to use peft-fine-tuning Skill
- Use it for developers using hermes-agent.
Built with
Editorial notes
Source
- Creator: NousResearch
- Repository: NousResearch/hermes-agent
- Skill file: optional-skills/mlops/peft/SKILL.md
What it does
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
Skill instructions
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 bash Basic installation pip install peft With quantization support (recommended) pip install peft bitsandbytes Full stack pip install peft transformers accelerate bitsandbytes datasets LoRA fine-tuning (standard
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Frequently asked questions
What is peft-fine-tuning?
peft-fine-tuning is a open-source AI agent skill with Copy skill directory. Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal.
Who is peft-fine-tuning best for?
peft-fine-tuning is best for reusing agent instructions, scripts, and references.
How do I install peft-fine-tuning?
Install or run peft-fine-tuning using Copy skill directory. Check peft-fine-tuning for the latest setup command.
Is peft-fine-tuning actively maintained?
peft-fine-tuning may need a closer maintenance check before production use.
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