Fine-tuning has emerged as a reliable method for improving the accuracy of pre-trained open-source models like Llama-2, cutting down on the time and computational resources needed compared to training a language model from scratch or investing in a costly commercial LLM.
But what does it take to successfully fine-tune a model?
Our definitive guide provides practical advice for overcoming the four primary challenges teams face when fine-tuning LLMs:
- When is the right time to fine-tune your models
- How to prepare your data for training
- What does it take to manage infra for training and serving
- How to reliably and efficiently fine-tune
By following these best practices, you will be able to fine-tune your LLMs effectively and efficiently, and unleash their full potential.
