LLM Fine-Tuning
Master the art of fine-tuning large language models for specific tasks and domains
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Modules
Introduction to Fine-Tuning
Learn the basics of fine-tuning large language models
Introduction to Fine-Tuning
Fine-tuning is the process of taking a pre-trained language model and further training it on a specific dataset for a particular task. This allows you to adapt a general-purpose model to your specific needs, improving performance and efficiency.
Why Fine-Tune?
- Improved Performance: Fine-tuning can significantly improve the accuracy and relevance of a language model for your specific task.
- Efficiency: Fine-tuning requires less data and compute than training a model from scratch.
- Customization: Tailor the model's behavior to match your desired output style and format.
When to Fine-Tune
- You have a specific task in mind (e.g., sentiment analysis, text summarization).
- You have a dataset of labeled examples for your task.
- You want to improve the performance of a pre-trained model on your task.