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peft-fine-tuning

peft-fine-tuning is a skill for Claude Code, Codex from itgoyo/hermes-skills. It costs 82 tokens per session (3,470 once invoked), scanned A, original, no licence file.

Technical guidance for parameter-efficient fine-tuning, which adapts a language model by training a small part of its parameters instead of the whole model.

In plain words
What is it for?
Use it to configure LoRA, QLoRA, other PEFT methods, and multi-adapter model serving.
Why use it?
It reduces the memory and computation needed to adapt large models, including when GPU memory is limited.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

not rated 79repo +4 6mo ago A scan 82 tokens no licence

Good fit Use it to configure LoRA, QLoRA, other PEFT methods, and multi-adapter model serving.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/itgoyo/hermes-skills/peft
View source ↗ itgoyo/hermes-skills
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add itgoyo/hermes-skills --skill peft
Clone the repo
git clone --depth 1 https://github.com/itgoyo/hermes-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for peft-fine-tuning

README.md
[![agentmods](https://agentmods.dev/badge/skills/itgoyo/hermes-skills/peft/github.svg)](https://agentmods.dev/skills/itgoyo/hermes-skills/peft)
Your own site
<a href="https://agentmods.dev/skills/itgoyo/hermes-skills/peft"><img src="https://agentmods.dev/badge/skills/itgoyo/hermes-skills/peft/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for peft-fine-tuning

Your own site · 80×15
<a href="https://agentmods.dev/skills/itgoyo/hermes-skills/peft"><img src="https://agentmods.dev/badge/skills/itgoyo/hermes-skills/peft.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,470 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00082 $0.03470
Opus 5.5 $0.00033 $0.01388
Sonnet 5.5 $0.00016 $0.00694
Haiku 4.5 $0.00008 $0.00347

Measured 2mo ago against content hash 79ec7275734c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

peft-fine-tuning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

mlops/training/peft/SKILL.md · 435 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2mo ago First seen · 435 lines · 82 tokens per session scan A 79ec7275734c

Subscribe to this mod's changes

peft-fine-tuning is a skill published in the GitHub repository itgoyo/hermes-skills (79 stars, last pushed 6mo ago), with no licence file. It adds 82 tokens to every session and 3,470 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Data and AI
Related

peft-fine-tuning compared with skills from other repositories

peft-fine-tuning next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
peft-fine-tuningitgoyo/hermes-skillsSkill796mo agononeA82Claude Code, Codex
agent-platform-model-registrygoogle/skillsSkill22kyesterdayApache-2.0A60Claude Code, Codex
foundry-config-setupmicrosoft/agent-frameworkSkill15kyesterdayMITA65Claude Code, Codex
google-cloud-solution-agentic-analytics-spark-knowledge-cataloggoogle/skillsSkill22kyesterdayApache-2.0A132Claude Code, Codex
training-checkwanshuiyin/Auto-claude-code-research-in-sleepSkill19k3d agoMITA35Claude Code
nemo-automodel-launcher-configNVIDIA/skillsSkill3.9kyesterdayApache-2.0A30Claude Code
finetuningmicrosoft/skillsSkill3.2kyesterdayMITA132Claude Code