Beginner's Guide to Building with Open Source LLM

Beginner's Guide to Building with Open Source LLM

25 Jun 202413:06pm - 25 Jun 202414:06pm

Beginner's Guide to Building with Open Source LLM

About the Event

Join this session for a comprehensive introduction to building applications with open-source Large Language Models (LLMs). We will cover essential concepts, including the intuition behind LLMs, tokenizers, encoders, and decoders, as well as the distinctions between encoder-decoder models.

Topics include quantization techniques, fine-tuning methods like LoRA and adapters, and alignment strategies including RHLF, DPO, PPO, and ORPO. Additionally, we'll discuss Retrieval-Augmented Generation (RAG) and key metrics for evaluating LLMs. We'll also address the drawbacks of using open-source LLMs and provide practical solutions to overcome these challenges. This session is designed to equip beginners with the knowledge and tools to effectively build applications using open-source LLMs.

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Who is this DataHour for?

  1. Best articles get published on Analytics Vidhya’s Blog Space
  2. Best articles get published on Analytics Vidhya’s Blog Space
  3. Best articles get published on Analytics Vidhya’s Blog Space

About the Speaker

Ramesh Manickam

Ramesh Manickam

Senior Data Scientist at Fractal

Ramesh Manickam is a Data Scientist with six years of industrial experience in NLP, CV, and LLMs. Currently serving as a Senior Data Scientist at Fractal, focuses on developing products that extract insights from structured data using text-to-SQL with open-source LLMs, primarily on the retail sector. In his previous roles, he contributed to the life sciences domain by developing products that streamlined data analysis and submission processes, leveraging document AI and open-source LLMs to derive insights from unstructured data effectively.

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