fine-tuning

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

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Quick Start

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

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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

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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

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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

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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

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Evaluation Framework

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

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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

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References

See: Hugging Face PEFT, OpenAI Fine-tuning Guide