Fine-Tuning Data Generator

This skill generates high-quality synthetic training data in ChatML format for fine-tuning language models using frameworks like Unsloth, Axolotl, or similar tools.

What Do I Need?

Need	Resource
Planning my dataset - requirements, strategy, quality checklist	resources/dataset-strategy.md
How to create diverse examples - variation techniques, multi-turn patterns, format-specific guidance	resources/generation-techniques.md
ChatML format details - structure, specification, common issues, framework compatibility	resources/chatml-format.md
Example datasets - inspiration across domains, multi-turn samples, edge cases	resources/examples.md
Validating quality - validation workflow, analyzing datasets, troubleshooting	resources/quality-validation.md
Training & deployment - framework setup, hyperparameters, optimization, deployment	resources/framework-integration.md

Workflow

Phase 1: Gather Requirements

Start with these essential clarifying questions:

Task Definition:

• What is the model being trained to do? (e.g., customer support, code generation, creative writing)
• What specific domain or subject matter? (e.g., legal, medical, e-commerce, software development)
• How many training examples are needed? (Recommend: 100+ for simple tasks, 500-1000+ for complex)

Quality & Diversity:

• Complexity range: simple to complex mix, or focus on specific difficulty level?
• Diversity: edge cases, error handling, unusual scenarios?
• Tone/style: professional, friendly, technical, concise, detailed?
• Response length preferences?
• Any specific formats: code blocks, lists, tables, JSON?

Dataset Composition:

• Distribution across subtopics: evenly distributed or weighted?
• Include negative examples (what NOT to do)?
• Need validation split? (Recommend 10-20% of total)

See resources/dataset-strategy.md for detailed question templates.

Phase 2: Create Generation Plan

Present a plan covering:

• Number and distribution of examples across categories
• Key topics/scenarios to cover
• Diversity strategies (phrasing variations, complexity levels, edge cases)
• System prompt approach (consistent vs. varied)
• Quality assurance approach

Get user approval before generating.

Phase 3: Generate Synthetic Data

Create examples following these quality standards:

Key Principles:

• Realistic scenarios reflecting real-world use cases
• Natural language with varied phrasing and formality levels
• Accurate, helpful responses aligned with desired behavior
• Consistent ChatML formatting throughout
• Balanced difficulty (unless specified)
• Meaningful variety (no repetition)
• Include edge cases and error scenarios

Diversity Techniques:

• Vary query phrasing (questions, commands, statements)
• Include different expertise levels (beginner, intermediate, expert)
• Cover both positive and negative examples
• Mix short and long-form responses
• Include multi-step reasoning when appropriate
• Add context variations

See resources/generation-techniques.md for detailed techniques, domain-specific guidance, and batch generation workflow.

Phase 4: Validate & Document

Run validation tools and checks:

# Validate JSON formatting and structure
python scripts/validate_chatml.py training_data.jsonl

# Analyze dataset statistics and diversity
python scripts/analyze_dataset.py training_data.jsonl

# Export statistics
python scripts/analyze_dataset.py training_data.jsonl --export stats.json

Quality Checklist:

• JSON validation passed (no errors)
• Analysis shows good diversity metrics
• Manual sample review passed
• No duplicate or near-duplicate examples
• All required fields present
• Realistic user queries
• Accurate, helpful responses
• Balanced category distribution
• Dataset metadata documented

See resources/quality-validation.md for validation details, troubleshooting, and documentation templates.

Phase 5: Integration & Training

Prepare for training with your framework of choice:

Output Files:

• training_data.jsonl - Main training set
• validation_data.jsonl - Optional validation set
• dataset_info.txt - Metadata and statistics

Framework Setup:

• Unsloth: Automatic ChatML detection, efficient 4-bit training
• Axolotl: Specify type: chat_template and chat_template: chatml
• Hugging Face: Use tokenizer's apply_chat_template() method
• Custom: Load from JSONL, handle ChatML formatting

See resources/framework-integration.md for setup code, hyperparameters, deployment options, and best practices.

ChatML Format Overview

Each training example is a JSON object with a messages array:

{"messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "How do I reverse a string in Python?"}, {"role": "assistant", "content": "Use slicing: `text[::-1]`"}]}

Roles:

• system: Sets assistant behavior (optional but recommended)
• user: User's input/query
• assistant: Model's expected response

Multi-turn: Add additional user/assistant message pairs for conversations.

See resources/chatml-format.md for detailed specification, validation, common issues, and framework-specific notes.

Tool Reference

Scripts in scripts/

validate_chatml.py

Validates ChatML format JSONL files:

python scripts/validate_chatml.py training_data.jsonl
python scripts/validate_chatml.py training_data.jsonl --verbose

Checks:

• Valid JSON formatting
• Required fields (messages, role, content)
• Valid role values (system, user, assistant)
• Proper message order
• Duplicate detection
• Diversity metrics

analyze_dataset.py

Provides comprehensive statistics and analysis:

python scripts/analyze_dataset.py training_data.jsonl
python scripts/analyze_dataset.py training_data.jsonl --export stats.json

Provides:

• Dataset overview (total examples, message counts)
• Message length statistics
• System prompt variations
• User query patterns (questions, commands, code-related, length categories)
• Assistant response patterns (code blocks, lists, headers, length categories)
• Quality indicators (diversity score, balance ratio)
• Token estimates and cost projection

Common Workflows

Small Dataset (100-200 examples)

1. Gather requirements
2. Create generation plan for 1-2 categories
3. Generate in single batch, review quality
4. Validate and document
5. Ready for training

Medium Dataset (500-1000 examples)

1. Gather requirements
2. Create detailed plan with multiple categories
3. Generate in 2-3 batches, reviewing after each
4. Analyze diversity and adjust approach
5. Fill any gaps
6. Final validation and documentation

Large Dataset (2000+ examples)

1. Gather comprehensive requirements
2. Create multi-batch generation plan
3. Batch 1 (50-100): Foundation examples
4. Batch 2 (100-200): Complexity expansion
5. Batch 3 (100-200): Coverage filling
6. Batch 4 (50-100): Polish and validation
7. Run full validation suite
8. Generate comprehensive documentation

Best Practices

Start Small, Iterate

1. Generate 10-20 examples first
2. Review and get feedback
3. Refine approach based on feedback
4. Scale up to full dataset

Quality Over Quantity

• Better to have 500 diverse, high-quality examples than 5,000 repetitive ones
• Each example should teach something new
• Maintain consistent response quality throughout

Diversify Systematically

• Vary query phrasing (questions, commands, statements)
• Cover different expertise levels
• Mix response complexities
• Include edge cases (typically 20-30% of dataset)
• Use batch generation workflow for large datasets

Test Before Deployment

• Test dataset with actual training framework
• Monitor training metrics for issues
• Test fine-tuned model outputs before deployment
• Compare results to base model

Document Everything

• Keep notes on generation parameters
• Save different dataset versions
• Document any modifications made
• Record generation strategies used
• Track model performance metrics

Advanced Features

Batch Generation Strategy

For datasets 500+ examples:

• Generate 50-100 examples at a time
• Review distribution and diversity after each batch
• Adjust generation strategy based on identified gaps
• Prevents repetition and maintains creativity

Common Pitfalls to Avoid

• Over-templating: Creates repetitive patterns (vary naturally)
• Unrealistic Queries: Overly formal/robotic user inputs (use varied phrasing)
• Narrow Coverage: Limited scenarios and phrasing (ensure diversity)
• Inconsistent Quality: Quality degradation over time (use quality checklist)
• JSON Errors: Invalid formatting breaking training (always validate)
• Missing Context: System prompts without detail (provide clear instructions)
• Response Mismatch: Responses don't address queries (verify relevance)

Dataset Size Recommendations

Task Complexity	Recommended Size	Notes
Simple tasks	100-500	Well-defined, limited variation
Medium tasks	500-2,000	Multiple scenarios, moderate complexity
Complex tasks	2,000-10,000+	Many edge cases, high variability
Domain adaptation	1,000-5,000	Specialized knowledge required

Resources

• Planning & Strategy: resources/dataset-strategy.md - Requirements gathering, planning, quality checklists
• Generation Techniques: resources/generation-techniques.md - Diversity techniques, domain-specific guidance, batch workflows
• ChatML Specification: resources/chatml-format.md - Format details, validation, framework notes
• Example Datasets: resources/examples.md - Diverse domain examples, multi-turn patterns
• Quality Validation: resources/quality-validation.md - Validation workflow, analysis, troubleshooting
• Framework Integration: resources/framework-integration.md - Setup for Unsloth, Axolotl, HuggingFace; deployment options

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Version: 2.0 | Updated: 2024 | Pattern: Modular Orchestration