Fine-tuning LLMs to Reduce Hallucination - Leveraging Out-of-Domain Data

Fine-tuning LLMs to Reduce Hallucination - Leveraging Out-of-Domain Data

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Setting Up the Demo: Instructions for setting up and running the demo notebook

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8 of 14

Setting Up the Demo: Instructions for setting up and running the demo notebook

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Classroom Contents

Fine-tuning LLMs to Reduce Hallucination - Leveraging Out-of-Domain Data

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  1. 1 Webinar agenda and overview of Mistral AI
  2. 2 Fine-Tuning Services: Introduction to Mistral's fine-tuning API and services
  3. 3 Conversational AI Interface: Introduction to LAT, Mistral's conversational AI tool
  4. 4 Latest Model Releases: Newest Mistral models and their features
  5. 5 Fine-Tuning Process: Steps and benefits of fine-tuning models
  6. 6 Hackathon Winning Projects: Examples of innovative uses of fine-tuning
  7. 7 Hands-On Demo Introduction: Introduction to the practical demo segment
  8. 8 Setting Up the Demo: Instructions for setting up and running the demo notebook
  9. 9 Creating Initial Prompt: Steps to create and test an initial prompt
  10. 10 Evaluation Pipeline: Setting up and running an evaluation pipeline for model performance
  11. 11 Improving Model Performance: Strategies and techniques to enhance model accuracy
  12. 12 Fine-Tuning and Results: Creating and evaluating a fine-tuned model
  13. 13 Two-Step Fine-Tuning: Explanation and demonstration of the two-step fine-tuning process
  14. 14 Conclusion and final thoughts

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