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To make LLM faster we need faster retrieval system. Here comes Embedding Quantization. Embedding quantization is great technique to save cost on Vector DB, significantly faster retrieval while preserving retrieval performance.
The Llama-2-GGML-CSV-Chatbot is a conversational tool leveraging the powerful Llama-2 7B language model. It facilitates multi-turn interactions based on uploaded CSV data, allowing users to engage in seamless conversations.
Implementing Vector Database on CoNaLa dataset to retrieve program snippets relevant to user queries. This is a very simple simulation of a Vector Database.
Llama2-Medical-Chatbot is a medical chatbot that uses the Llama-2-7B-Chat-GGML model and the pdf The Gale Encyclopedia of Medicine, Volume 1, 2nd Edition. It is still under development, but it has the potential to be a valuable tool for patients, healthcare professionals, and researchers.