Axolotl and Fine-Tuning Strategies

Introduction

• Today's agenda:
  • Discuss Axolotl's usage
  • Review honeycomb example from previous session
  • Q&A with Wing
  • Zach's presentation on parallelism and Hugging Face Accelerate
  • Q&A session

Key Considerations for Fine-Tuning

Model Capacity

• Common questions for beginners:
  • What model to fine-tune?
  • Should I use LoRa or full fine-tune?

Base Model Selection

1. Model Size:

   • Options: 7B, 13B, 70B, etc.
   • Recommendation: Use 7B models for most cases due to faster performance and easier GPU allocation.
   • Popularity indicated by download counts.

2. Model Family:

   • Examples: Llama 2, Llama 3, Mistral, Zephyr, Gemma.
   • Choose current or trending models for testing (e.g., Llama 3).
   • Community resources: Hugging Face, Local Llama subreddit.

LoRa vs Full Fine-Tuning

• LoRa (Low-Rank Adaptation) is often preferred:
  • Reduces number of parameters to train.
  • Easier on GPU memory constraints.
  • Full fine-tunes might offer higher performance but are resource-intensive.

Understanding LoRa

• Concept:
  • LoRa uses low-rank matrices to adjust weights, making it less resource-heavy.
  • Compared to full fine-tuning, LoRa has significantly fewer parameters (e.g., 128,000 vs 16 million).

Key Takeaways about LoRa

• Most fine-tuning in practice utilizes LoRa.
• QLoRa is an extension that quantizes weights, further saving memory.
  • Performance impact is minimal but ensures efficient storage.

Transitioning to Implementation

Using Axolotl

• Axolotl simplifies the fine-tuning process, allowing users to focus on data rather than code errors.
• Configuration: Use YAML config files, often starting from examples.
• Important settings to customize: Data set path, loss functions, etc.

Steps to Get Started

1. Run Preprocessing: Prepares data for training.
2. Train the Model: Use the command line interface to execute training.
3. Testing and Evaluation: Check model outputs against expected results.

Honeycomb Case Study

• Honeycomb aims to simplify querying through natural language instead of using HQL (Honeycomb Query Language).
• Evaluations: Write unit tests and assertions to ensure model outputs are valid.
• Data synthesis: Generating additional data points to improve model performance.

Debugging and Best Practices

• Importance of evaluating your data and outputs.
• Learn to iterate on your evaluation process and test results.

Modal Integration

• Modal: A cloud-native platform for running Python code remotely.
• Ideal for hyperparameter tuning and model training with Axolotl.
• Example provided for integrating Axolotl with Modal for simplified training processes.

Conclusion

• Fine-tuning LLMs effectively requires understanding model choices, efficient use of tools like LoRa, and leveraging frameworks like Axolotl and Modal.
• Continuous evaluation and iteration are key to improving model outputs.