The author also provides a detailed explanation of how to create a dataset, select metrics and baseline, and create a baseline with existing models. The author used the Low-Rank Adaptation (LoRA) technique for fine-tuning and used the lit-gpt tool from Lightning AI. After fine-tuning, the author converted the model to GGUF format using LLaMa.cpp and tested it. The author concludes by acknowledging that they are still learning and improving their methods.
Key takeaways:
- The author discusses the process of fine-tuning a Language Model (LLM) for a specific scenario, in this case, correcting grammar, spelling, punctuation, and capitalization in sentences.
- The author uses the LLaMA.cpp tool on a Mac to leverage the model and discusses the steps involved in data generation and fine-tuning the model.
- The author also provides a detailed guide on creating a dataset, selecting metrics, creating a baseline with existing models, and fine-tuning using the LoRA technique.
- Finally, the author explains how to use the fine-tuned model with LLaMA.cpp, including the steps to convert the model to GGUF format and run it.