Enhancing Llama2’s proficiency in Python through supervised fine-tuning and low-rank adaptation techniques
Our previous article covered Llama 2 in detail, presenting the family of Large Language models (LLMs) that Meta introduced recently and made available for the community for research and commercial use. There are variants already designed for specific tasks; for example, Llama2-Chat for chat applications. Still, we might want to get an LLM even more tailored for our application.
Following this line of thought, the technique we are referring to is transfer learning. This approach involves leveraging the vast knowledge already in models like Llama2 and transferring that understanding to a new domain. Fine-tuning is a subset or specific form of transfer learning. In fine-tuning, the weights of the entire model, including the pre-trained layers, are typically allowed to adjust to the new data. It means that the knowledge gained during pre-training is refined based on the specifics of the new task.
In this article, we outline a systematic approach to enhance Llama2’s proficiency in Python coding tasks by fine-tuning it on a custom dataset. First, we curate and align a dataset with Llama2’s prompt structure to meet our objectives. We then use Supervised Fine-Tuning (SFT) and Quantized Low-Rank Adaptation (QLoRA) to optimize the Llama2 base model. After optimization, we combine our model’s weights with the foundational Llama2. Finally, we showcase how to perform inference using the fine-tuned model and how does it compare against the baseline model.
One important caveat to recognize is that fine-tuning is sometimes unnecessary. Other approaches are easier to implement and, in some cases, better suited for our use case. For example, semantic search with vector databases efficiently handles informational queries, leveraging existing knowledge without custom training. The use cases where fine-tuning is required is when we need tailored interactions, like specialized Q&A or context-aware responses that use custom data.