The guide also provides instructions on how to check the model's performance, set up tokenization settings, and prepare the model for int8 training. The author uses a variety of libraries and tools such as Python 3.10, cuda 11.8, Huggingface Hub, and Weights and Biases to execute the process. The guide concludes with the loading of the final checkpoint and testing the model's output, which proves successful. The author suggests following another guide to convert the adapter to a Llama.cpp model for local execution.
Key takeaways:
- The guide provides a detailed process of fine-tuning Code Llama to enhance its performance as an SQL developer, using a specific dataset and a Lora approach.
- The process involves several steps including pip installs, loading libraries, loading the dataset, loading the model, checking the base model, tokenization, setting up Lora, and training.
- The guide also provides code snippets for each step, making it easier for users to follow along and implement the process.
- After training, the model is tested to verify its performance. The guide concludes that the fine-tuned model works effectively, providing the correct SQL query output.