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fine-tuning

@pluginagentmarketplace/custom-plugin-prompt-engineering
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LLM fine-tuning and prompt-tuning techniques

Install Skill

1Download skill
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Click "Upload skill" and select the downloaded ZIP file

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SKILL.md

name fine-tuning
description LLM fine-tuning and prompt-tuning techniques
sasmp_version 1.3.0
bonded_agent 05-prompt-optimization-agent
bond_type PRIMARY_BOND

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