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Fine-tuning DNA Language Models

This section provides comprehensive tutorials and guides for fine-tuning DNA language models using DNALLM. Fine-tuning allows you to adapt pre-trained models to your specific DNA analysis tasks and datasets.

What You'll Learn

  • Basic Fine-tuning: Get started with simple model adaptation
  • Advanced Techniques: Custom loss functions, data augmentation, and optimization
  • Task-Specific Guides: Classification, generation, and specialized tasks
  • Best Practices: Hyperparameter tuning, monitoring, and deployment

Quick Navigation

Topic Description Difficulty
Getting Started Basic fine-tuning setup and configuration Beginner
Task-Specific Guides Fine-tuning for different task types Intermediate
Advanced Techniques Custom training, optimization, and monitoring Advanced
Configuration Guide Detailed configuration options and examples Intermediate
Examples and Use Cases Real-world fine-tuning scenarios All Levels
Troubleshooting Common issues and solutions All Levels

Prerequisites

Before diving into fine-tuning, ensure you have:

  • ✅ DNALLM installed and configured
  • ✅ Access to pre-trained DNA language models
  • ✅ Training datasets in appropriate formats
  • ✅ Sufficient computational resources (GPU recommended)
  • ✅ Understanding of your target task and data

Quick Start

from dnallm import load_config, load_model_and_tokenizer, DNADataset, DNATrainer

# Load configuration
config = load_config("finetune_config.yaml")

# Load pre-trained model and tokenizer
model, tokenizer = load_model_and_tokenizer(
    "zhangtaolab/plant-dnabert-BPE",
    task_config=config['task'],
    source="huggingface"
)

# Load and prepare dataset
dataset = DNADataset.load_local_data(
    "path/to/your/data.csv",
    seq_col="sequence",
    label_col="label",
    tokenizer=tokenizer,
    max_length=512
)

# Initialize trainer and start fine-tuning
trainer = DNATrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset,
    config=config
)

trainer.train()

Supported Task Types

Task Type Description Use Cases
Classification Binary, multi-class, and multi-label classification Promoter prediction, motif detection, functional annotation
Generation Sequence generation and completion DNA synthesis, sequence design, mutation analysis
Masked Language Modeling Sequence completion and prediction Sequence analysis, mutation prediction
Token Classification Named entity recognition and tagging Gene identification, regulatory element detection
Regression Continuous value prediction Expression level prediction, binding affinity

Key Features

  • Flexible Architecture: Support for various model architectures (BERT, GPT, Transformer variants)
  • Task-Specific Heads: Automatic head selection based on task type
  • Data Processing: Built-in DNA sequence preprocessing and augmentation
  • Training Optimization: Mixed precision, gradient accumulation, and scheduling
  • Monitoring: TensorBoard integration and comprehensive logging
  • Checkpointing: Automatic model saving and resumption

Model Sources

  • Hugging Face Hub: Access to thousands of pre-trained models
  • ModelScope: Alternative model repository with specialized models
  • Local Models: Use your own pre-trained models
  • Custom Architectures: Implement and fine-tune custom model designs

Next Steps

Choose your path:

Need Help? Check our FAQ or open an issue on GitHub.