The demand for applications powered by large language models (LLMs) is increasing, from chatbots to virtual assistants to content generation. However, to achieve optimal performance and accuracy, it is necessary to fine-tune these models on specific tasks and domains. Traditionally, finetuning involved updating the weights of all layers in the model, which can be time-consuming and require extensive computational resources.  T-Few finetuning is an additive Parameter Efficient Finetuning technique that inserts additional layers, comprising approximately 0.01% of the baseline model's size. It adds 1D vectors L_K, L_V, and L_FF that are multiplied with the K, V, and feed-forward weights during inference. Overview of T-Few Finetuning T-Few finetuning is an additive Parameter Efficient Finetuning technique that inserts additional layers, comprising approximately 0.01% of the baseline model's size. Specifically, it adds 1D vectors L_K, L_V, and L_FF that are multiplied with the K

  LangChain is an open-source Python framework designed to facilitate the development of applications powered by l arge language models (LLMs) . It offers a suite of tools, components, and interfaces that simplify the construction of LLM-centric applications. With LangChain, developers can manage interactions with language models, seamlessly link different components, and incorporate resources such as APIs and databases. LangChain enables developers to build applications that can generate creative and contextually relevant content, such as blog articles, product descriptions, chatbots, Generative Question-Answering (GQA), summarization, and much more. Introduction to LangChain LangChain is a powerful, open-source framework designed to help developers develop applications powered by a language model, particularly a large language model (LLM). It is a collection of APIs that developers can embed in their applications, empowering them to infuse language processing capabilities without ha