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