Axolotl Fine-Tuning Tips & Tricks: A Comprehensive Guide

tips and tricks from the community

Dataset Preparation and Management

• Use python -m axolotl.cli.preprocess your_config.yml --debug for preprocessing data and debugging the output. This ensures data is in the correct format for training or fine-tuning.

• Utilize dataset_shard_num and dataset_shard_idx parameters to work with dataset fractions, especially for large datasets or limited computational resources.

• Reduce dataset size when working with extremely large datasets to manage training duration effectively while maintaining or improving quality.

• Be aware of the datasets library caching intermediate transforms during preparation, as it can affect disk space usage and training efficiency.

• Define custom dataset formats for unique data requirements to allow for more tailored data handling.

• Handle duplicate data entries appropriately to prevent data contamination or model confusion.

• Be cautious with datasets containing anomalies like duplicates or conflicting entries, as they can introduce noise into training. Design preprocessing steps to handle or remove such anomalies effectively.

Hardware and Environment Setup

• Set up multi-GPU support for efficient training, potentially using DeepSpeed for distributed training.

• Ensure compatibility between precision settings and available libraries or dependencies.

• Be mindful of how different libraries and tools interact, and consult relevant documentation for guidance.

• Ensure the correct CUDA toolkit version is installed and matches library and framework requirements.

• Optimize hardware use by selecting the correct GPU architecture and ensuring dependencies like CUDA are up to date. Use optimized tools like bitsandbytes-rocm for specific hardware.

Model Configuration and Training

• Create custom prompt fields for tailoring model responses to specific needs and context-relevant interactions.

• Structure data correctly using appropriate separator styles, custom tokens, and formats like JSON for efficient training.

• Integrate DeepSpeed, using versions like zero2 and zero3, to enhance training efficiency, especially in multi-GPU setups.

• Adjust evaluation dataset size or configuration to avoid issues like 'eval_loss' returning NaN.

• Understand and correctly set configuration file parameters like load_in_8bit, load_in_4bit, and strict for specific use cases.

• Tweak adapter settings, such as changing to adapter: lora, for potentially significant impact on model performance or compatibility.

• Configure accelerator settings correctly for specific hardware and model requirements, using tools like the accelerate library.

• Manage sequence length and sample packing for models like Llama or Mixtral to impact performance and training efficiency. Larger sequence lengths require more memory but can improve contextual understanding, while sample packing can increase throughput at the cost of complexity.

• Experiment with learning rates and schedulers to help models quickly converge and refine predictions.

• Use techniques like gradient checkpointing, mixed precision training, and optimizers like AdamW with reduced precision to manage VRAM usage and balance training speed.

• Explore advanced optimization techniques like quantization and pruning to reduce model size and speed up inference without significantly impacting performance.

• Leverage DeepSpeed's ZeRO stages and ensure configuration is optimized for your hardware setup to reduce memory requirements and maximize GPU utilization.

• For large models, consider using Fully Sharded Data Parallel (FSDP) to distribute model parameters across multiple GPUs, reducing memory requirements per GPU.

Tokenizer and Special Tokens Configuration

• Specify tokenizer configuration in the YAML file to ensure data preprocessing aligns with model expectations, especially with custom features like ChatML.

• Ensure tokenizer configuration accurately reflects the tokens your model expects, especially with custom or complex models, to avoid unexpected behavior or inefficient training.

• Maintain consistency in special token configuration (e.g., BOS/EOS tokens) across data preprocessing, model training, and inference to avoid discrepancies.

Debugging and Problem Solving

• Use python -m axolotl.cli.preprocess your_config.yml --debug for preprocessing data and debugging the output.

• Monitor training progress and visualize results using tools like Weights & Biases (W&B) to understand model performance over time.

• Double-check version compatibility between training scripts, the Axolotl library, and model architecture when encountering errors related to unexpected arguments or missing keys in state dictionaries.

• Use gradient checkpointing to manage memory usage effectively, especially when training very large models on limited hardware.

• Ensure training setup correctly handles resuming from checkpoints, especially with tools like DeepSpeed, to avoid lost progress or errors.

• Be aware of specific hardware requirements for features like Flash Attention, as incompatible hardware can lead to runtime errors.

• Maintain consistency in tokenization between training and inference stages to avoid poor model performance and unexpected behavior.

• Double-check tokenizer settings and input configurations when encountering errors related to tokenization or input processing.

• Use debugging tools like Python's pdb to step through code and find elusive bugs.

• Investigate memory usage, data loading issues, or potential deadlocks in the training loop if training gets stuck, especially after saving checkpoints or at specific steps.

• Ensure model resumption from checkpoints is configured correctly to avoid training starting anew or errors due to missing or mismatched state dictionaries.

Logging and Monitoring

• Set a fixed seed in the training configuration to ensure deterministic sample packing, stabilizing training and making loss curves more predictable.

• Use detailed logging and integrate tools like Weights & Biases (WandB) to track training progress and diagnose issues, particularly with complex models or large-scale datasets.

• Integrate training with monitoring tools like TensorBoard for real-time tracking of metrics to quickly identify overfitting, underfitting, or performance deviations.

• Set up detailed logging, especially for long training runs on large datasets, as logs provide invaluable insights when troubleshooting or replicating experiments.

Continuous Learning and Adaptation

• Keep training scripts, model definitions, and dependencies up-to-date with the latest practices and libraries to avoid compatibility issues and leverage improvements in architectures, algorithms, and hardware support.

• Regularly update libraries and stay informed about new features and best practices in machine learning and tools like Axolotl, as they are constantly evolving.

By applying these tips and tricks, you can enhance your model training process using Axolotl, address common pitfalls, and optimize your models for performance and accuracy effectively. Remember to stay updated with the latest developments in the field and adapt your approach based on the specific needs of your project.