How do I install the Fine Tuning skill?

Run npx skillmds@latest add majiayu000/fine-tuning-3 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Fine Tuning skill do?

LLM fine-tuning and prompt-tuning techniques It is listed under AI & ML on SkillMD.

Is Fine Tuning safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Fine Tuning?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Fine Tuning free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Fine Tuning?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Fine Tuning by majiayu000 — Agent Skill

Name: Fine Tuning
Rating: 4.2 (5 reviews)
Author: majiayu000

File contents

Fine-Tuning Skill

Bonded to: prompt-optimization-agent

Quick Start

Skill("custom-plugin-prompt-engineering:fine-tuning")

Parameter Schema

parameters:
  tuning_method:
    type: enum
    values: [full, lora, qlora, prompt_tuning, prefix_tuning]
    default: lora

  dataset_size:
    type: enum
    values: [small, medium, large]
    description: "<1k, 1k-10k, >10k examples"

  compute_budget:
    type: enum
    values: [low, medium, high]
    default: medium

Tuning Methods Comparison

Method	Parameters	Compute	Quality	Best For
Full Fine-tune	All	Very High	Highest	Maximum customization
LoRA	~0.1%	Low	High	Resource-constrained
QLoRA	~0.1%	Very Low	Good	Consumer GPUs
Prompt Tuning	<0.01%	Minimal	Good	Simple tasks
Prefix Tuning	~0.1%	Low	Good	Generation tasks

Dataset Preparation

Format Templates

formats:
  instruction:
    template: |
      ### Instruction
      {instruction}

      ### Response
      {response}

  chat:
    template: |
      <|user|>
      {user_message}
      <|assistant|>
      {assistant_response}

  completion:
    template: "{input}{output}"

Quality Criteria

quality_checklist:
  - [ ] No duplicate examples
  - [ ] Consistent formatting
  - [ ] Diverse examples
  - [ ] Balanced categories
  - [ ] High-quality outputs
  - [ ] No harmful content

Training Configuration

training_config:
  hyperparameters:
    learning_rate: 2e-5
    batch_size: 8
    epochs: 3
    warmup_ratio: 0.1

  lora_config:
    r: 16
    alpha: 32
    dropout: 0.05
    target_modules: ["q_proj", "v_proj"]

  evaluation:
    eval_steps: 100
    save_steps: 500
    metric: loss

Evaluation Framework

Metric	Purpose	Target
Loss	Training progress	Decreasing
Accuracy	Task performance	>90%
Perplexity	Model confidence	<10
Human eval	Quality assessment	Preferred >80%

Troubleshooting

Issue	Cause	Solution
Overfitting	Small dataset	Add regularization
Underfitting	Low epochs	Increase training
Catastrophic forgetting	Aggressive tuning	Lower learning rate
Poor generalization	Data bias	Diversify dataset

References

See: Hugging Face PEFT, OpenAI Fine-tuning Guide

---name: fine-tuning-3description: LLM fine-tuning and prompt-tuning techniques--- # Fine-Tuning Skill **Bonded to:** `prompt-optimization-agent` --- ## Quick Start ```bash Skill("custom-plugin-prompt-engineering:fine-tuning") ``` --- ## Parameter Schema ```yaml parameters: tuning_method: type: enum values: [full, lora, qlora, prompt_tuning, prefix_tuning] default: lora dataset_size: type: enum values: [small, medium, large] description: "<1k, 1k-10k, >10k examples" compute_budget: type: enum values: [low, medium, high] default: medium ``` --- ## Tuning Methods Comparison | Method | Parameters | Compute | Quality | Best For | |--------|-----------|---------|---------|----------| | Full Fine-tune | All | Very High | Highest | Maximum customization | | LoRA | ~0.1% | Low | High | Resource-constrained | | QLoRA | ~0.1% | Very Low | Good | Consumer GPUs | | Prompt Tuning | <0.01% | Minimal | Good | Simple tasks | | Prefix Tuning | ~0.1% | Low | Good | Generation tasks | --- ## Dataset Preparation ### Format Templates ```yaml formats: instruction: template: | ### Instruction {instruction} ### Response {response} chat: template: | <|user|> {user_message} <|assistant|> {assistant_response} completion: template: "{input}{output}" ``` ### Quality Criteria ```yaml quality_checklist: - [ ] No duplicate examples - [ ] Consistent formatting - [ ] Diverse examples - [ ] Balanced categories - [ ] High-quality outputs - [ ] No harmful content ``` --- ## Training Configuration ```yaml training_config: hyperparameters: learning_rate: 2e-5 batch_size: 8 epochs: 3 warmup_ratio: 0.1 lora_config: r: 16 alpha: 32 dropout: 0.05 target_modules: ["q_proj", "v_proj"] evaluation: eval_steps: 100 save_steps: 500 metric: loss ``` --- ## Evaluation Framework | Metric | Purpose | Target | |--------|---------|--------| | Loss | Training progress | Decreasing | | Accuracy | Task performance | >90% | | Perplexity | Model confidence | <10 | | Human eval | Quality assessment | Preferred >80% | --- ## Troubleshooting | Issue | Cause | Solution | |-------|-------|----------| | Overfitting | Small dataset | Add regularization | | Underfitting | Low epochs | Increase training | | Catastrophic forgetting | Aggressive tuning | Lower learning rate | | Poor generalization | Data bias | Diversify dataset | --- ## References See: Hugging Face PEFT, OpenAI Fine-tuning Guide

Frequently asked questions

Fine Tuning