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Large Language Models

Experiment with LLMs to drive change by identifying key elements and processes where untapped opportunities exist to generate value.

Which LLM is best for each purpose to scale a business most effectively?

Analysis Tools​

Tools for analysis of LLMs:

Leaderboards​

Fine-Tuning​

Best practice checklist for LLM fine-tuning:

  1. Define clear objectives: Establish specific goals for fine-tuning, such as improving performance on particular tasks or domains.
  2. Prepare high-quality data: Curate a diverse, representative, and clean dataset tailored to your specific use case.
  3. Choose the right base model: Select an appropriate pre-trained model that aligns with your task and computational resources.
  4. Determine the fine-tuning approach: Decide between full fine-tuning, parameter-efficient fine-tuning (e.g., LoRA, P-tuning), or prompt engineering based on your requirements and resources.
  5. Set up proper evaluation metrics: Define relevant performance metrics and establish a robust evaluation framework.
  6. Implement data augmentation: Use techniques like back-translation or paraphrasing to increase dataset diversity and prevent overfitting.
  7. Apply regularization techniques: Implement methods like weight decay or dropout to prevent overfitting during fine-tuning.
  8. Optimize hyperparameters: Conduct systematic hyperparameter tuning to find the optimal configuration for your specific task.
  9. Monitor training progress: Regularly assess model performance during training to detect and address issues like overfitting or underfitting.
  10. Validate on held-out data: Use a separate validation set to ensure the model generalizes well to unseen data.
  11. Implement ethical considerations: Address potential biases and ensure responsible AI practices throughout the fine-tuning process.
  12. Document the process: Maintain detailed records of your fine-tuning experiments, including data sources, model configurations, and results.
  13. Iterate and refine: Continuously improve your model based on evaluation results and new insights.
  14. Ensure scalability: Design your fine-tuning pipeline to accommodate future updates and larger datasets.
  15. Implement version control: Use proper versioning for both data and model checkpoints to ensure reproducibility.