Fine-Tuning Skill Skill

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

Agent Skills: Fine-Tuning Skill

Skill Files