Fine-Tune Your Own Llama 2 Model in a Colab Notebook

With the release of LLaMA v1, we saw a Cambrian explosion of fine-tuned models, including Alpaca, Vicuna, and WizardLM, among others. This trend encouraged different businesses to launch their own base models with licenses suitable for commercial use, such as OpenLLaMA, Falcon, XGen, etc. The release of Llama 2 now combines the best elements from both sides: it offers a highly efficient base model along with a more permissive license. During the first half of 2023, the software landscape was significantly shaped by the widespread use of APIs (like OpenAI API) to create infrastructures based on Large Language Models (LLMs). Libraries such as LangChain and LlamaIndex played a critical role in this trend. Moving into the latter half of the year, the process of fine-tuning (or instruction tuning) these models is set to become a standard procedure in the LLMOps workflow. This trend is driven by various factors: the potential for cost savings, the ability to process confidential data, and even the potential to develop models that exceed the performance of prominent models like ChatGPT and GPT-4 in certain specific tasks. In this article, we will see why instruction tuning works and how to implement it in a Google Colab notebook to create your own Llama 2 model. As usual, the code is available on Colab and GitHub. <a href="https://towardsdatascience.com/fine-tune-your-own-llama-2-model-in-a-colab-notebook-df9823a04a32">Website</a>