Open Source LLMs vs Closed Source LLMs
Large Language models are all over the place. Because of the rise of Large Language models, AI came into the limelight in the market. From development to the business world, most tasks are now automated thanks to the capability of Large Language Models. One breakthrough in this field was the release of ChatGPT by OpenAI. Large Language Models are autoregressive models trained on large amounts of data and can further perform tasks such as Text Generation, Text Autocompletion, Question and answering, Intelligent Chatbot, Code Generation, Text Summarization and more. Large Language models such as GPT are good, but they also address the issues of Data Privacy.
Large Language models such as ChatGPT that is GPT3.5 and closed source LLMs. By closed Large Languages we mean the model weights are not revealed publicly. This is where APIs are provided and not many enterprises rely on such services. This is where Open Source LLMs are one viable alternative to such concerns. Open-source Source Large Language models are such models whose weights are publicly available, and anyone can use these fine-tuned models on general specific data. Some of the popular open source LLMs are Mixtral, Llama, Falcon, Orca and so on.
Build RAG pipeline using Open Source Large Language Models
In this article, we will implement Retrieval Augmented Generation aka RAG pipeline using Open-Source Large Language models with Langchain and HuggingFace.
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