Fine-tuning a large language model (LLM) refers to the process of further training a pre-trained model on a specific task or domain. This allows the fine-tuned model to use its broad pre-training foundation and specialize it for your specific task.

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Benefits of Fine-tuning

The fine-tuning process typically results in several benefits:

Increased Accuracy

Over off-the-shelf, pretrained models on the given task

Model Efficiency

Fine-tuning smaller models that can match or exceed the performance of larger models

Reduced Hallucinations

Teaches the model how to respond to different user inputs

Cost Reduction

Reduces the need for lengthy prompts and context in production

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When to Fine-tune

Here are some common use-cases for fine-tuning

Tailoring Style or Tone

Adjust the language model’s output to match specific writing styles or tones required for different applications like corporate communication, technical documentation, customer service responses, and creative writing.

Improving Output Structure

Teach the model to produce consistent formatting or output structures like JSON/XML generation, structured data extraction, API response formatting, and report generation.

Handling Edge Cases

Refine the model to effectively address various exceptional scenarios like domain-specific terminology, uncommon data formats, special use cases, and error handling.

Domain Specialization

Adapt the model to excel in specific fields like medical diagnosis, legal analysis, financial forecasting, or scientific research by incorporating domain-specific knowledge and terminology.

Multilingual Adaptation

Enhance the model’s capabilities across different languages, including handling cultural nuances, idioms, and region-specific expressions for global applications.

Safety & Alignment

Train the model to follow safety guidelines, ethical principles, and alignment requirements while maintaining helpfulness and avoiding harmful outputs.

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Factors Affecting Training

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Training Time and Cost

Fine-tuning on Predibase runs on Nvidia A100 80GB GPUs. Training time depends on:
  • Dataset size: Larger datasets take longer to train
  • Model type: Larger models with more parameters take longer
  • Training strategy: Different approaches (LoRA, full fine-tuning) have different time requirements

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Dataset Requirements

The number of samples needed depends on:
  • Type of task (SFT vs Cont. Pretraining vs GRPO)
  • How much your task differs from pretrained knowledge
  • Quality and diversity of your fine-tuning dataset
  • Size of the base model
  • Complexity of the task
Recommendations by task type:

SFT


  • Minimum: 500-1,000 examples
  • Optimal: 2,000-5,000 examples
  • Focus: High-quality instruction-response pairs

Cont. Pretraining


  • Minimum: 10,000 - 20,000 examples
  • Optimal: 100,000 - 1,000,000 examples
  • Focus: Domain-specific content and long-term context

RFT/GRPO


  • Minimum: 10 - 20 examples
  • Optimal: 100 - 1,000 examples
  • Focus: Diversity in difficulty of prompts for the task

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Token Limits

Each model has specific context window limitations:
  • Training data must fit within the model’s context window
  • Longer sequences will be truncated
  • Consider model-specific limits when preparing data
Check model-specific limits →

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Best Practices

  1. Start Small and Iterate
    • Begin with a focused dataset of 100-500 high-quality examples
    • Validate your approach and data format before scaling
    • Iterate based on initial results and feedback
    • Gradually increase dataset size while monitoring performance
  2. Ensure Data Quality
    • Maintain consistent formatting across all examples
    • Thoroughly clean data by removing errors, duplicates, and outliers
    • Balance your dataset across different scenarios and edge cases
    • Include diverse examples that represent the full range of use cases
    • Consider using data augmentation for underrepresented cases
  3. Choose the Right Model
    • Select model size based on task complexity and dataset size
    • Review and comply with model licensing requirements
    • Balance training costs against expected performance gains
    • Verify hardware requirements and resource availability
    • Consider using smaller models for initial experiments
  4. Implement Robust Evaluation
    • Define clear, measurable success metrics upfront
    • Create representative test sets that cover edge cases
    • Establish baseline performance using the base model
    • Regularly evaluate on held-out test data
    • Monitor for signs of overfitting and performance degradation
    • Track both quantitative metrics and qualitative outputs

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Next Steps