Review of the Honeycomb example introduced previously
Interactive Q&A session
Overview by Zack on parallelism and Hugging Face Accelerate
Quick run-through of fine-tuning on Modal
Closing Q&A session
Model Capacity Questions
Key Questions When Fine-Tuning
What model should I fine-tune off of?
Model size: 7B, 13B, 70B, etc.
Model family: Llama 2, Llama 3, Mistral, etc.
Should I use LoRA or full fine-tune?
Recommendation: Use LoRA for efficiency unless specific circumstances justify a full fine-tune.
Model Size Insights
Experience with Different Sizes:
7B and 13B models often yield comparable results.
70B models can be complex to manage and require more parallelism.
7B models are popular and easier to work with, especially when sufficient.
Choosing a Model Family
Use recent models like Llama 3 for good performance.
Check platforms like Hugging Face to find trending models.
Running multiple models for comparison can be beneficial, but often the most popular models suffice.
LoRA vs. Full Fine-Tuning
Understanding LoRA:
LoRA involves adding lower-dimensional matrices to the original weight matrices, significantly reducing the number of parameters needed (128,000 vs. 16 million).
LoRA generally requires less GPU RAM and is easier to implement.
Practitioners are encouraged to use LoRA initially with potential future full fine-tunes if needed.
Quantized LoRA (QLoRA):
QLoRA reduces memory usage further by quantizing weights, leading to memory savings.
Commonly used but may not drastically impact performance as expected.
Importance of Data Quality
Emphasis on Data Improvement:
Prioritize improving data quality over over-optimizing model parameters.
Enhancing data can lead to significant performance gains.
Getting Started with Axel
Initial Steps
Visit GitHub Axel repository for examples and quick-start documentation.
Work with YAML config files and modify existing examples to fit your dataset and needs.
Configuring Data Sets
Axel supports various data formats; specify the format correctly for best results.
Sample data formats and examples for training need careful preparation.
Training Process in Axel
Run the Pre-Processing Command: Ensure data is in the correct format.
Run the Training Command: Start training based on the configured settings.
Sanity Check the Model: Verify output using Hugging Face or Axel directly.
Honeycomb Case Study Overview
Use Case: Honeycomb is an observability platform allowing natural language queries instead of using a specific query language.
Focus on fine-tuning models to improve user query effectiveness.
Data evaluation is critical and includes unit tests and A/B testing.
Synthetic data generation is a method used to expand training datasets when real examples are limited.
Custom Evaluations
Writing evaluations for performance during training can be done through custom configurations.
There are various levels of evaluation from unit tests to A/B testing.
Debugging and Troubleshooting in Axel
Best practices include:
Use the latest version of Axel.
Reduce complexity by minimizing concurrency.
Regularly clear caches to avoid unexpected behavior.
Scaling Model Training with Hugging Face Accelerate (by Zack)
Understanding GPU Usage
Different models require varying amounts of GPU resources for training.
Distributed training techniques can manage resources more effectively across multiple GPUs.
Fully Sharded Data Parallelism (FSDP)
FSDP allows splitting model parameters across multiple GPUs, enabling training of larger models than a single GPU can handle.
Important strategies include sharding model states and parameters to optimize memory usage.
Using Accelerate with Axel
Configuration involves defining environment settings and memory estimations to ensure efficient training.
Key commands include accelerate config and accelerate launch.
Hyperparameter Tuning with Modal
Modal is a cloud-native platform that simplifies running Python code for model training.
Useful for hyperparameter tuning, provides real-time feedback and iterative development.
Final Thoughts and Recommendations
Focus on data quality and structuring before diving deep into hyperparameter optimization.
Regularly leverage tools like weights and biases for logging performance and metrics.
Stay updated with community resources and documentation to maximize the use of Axel and other frameworks.