How to Improve your LLM? Find the Best & Cheapest Solution
In this video, we dive into the world of large language models (LLMs) and discover the optimal techniques for your specific tasks! Learn when to choose between training from scratch, fine-tuning, (advanced) prompt engineering and Retrieval Augmented Generation (RAG) with Activeloop’s Deep Memory. Equip yourself with the knowledge to enhance LLM performance, balancing quality, costs, and ease of use. ✨🚀
► Jump on our free LLM course from the Gen AI 360 Foundational Model Certification (Built in collaboration with Activeloop, Towards AI, and the Intel Disruptor Initiative): https://learn.activeloop.ai/courses/llms/?utm_source=social&utm_medium=youtube&utm_campaign=llmcourse
With the great support of Cohere & Lambda.
► Course Official Discord: https://discord.gg/learnaitogether
► Activeloop Slack: https://slack.activeloop.ai/
► Activeloop YouTube: https://www.youtube.com/@activeloop
►Follow me on Twitter: https://twitter.com/Whats_AI
►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/
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How to start in AI/ML - A Complete Guide:
►https://www.louisbouchard.ai/learnai/
Become a member of the YouTube community, support my work and get a cool Discord role :
https://www.youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg/join
Chapters:
0:00 When do what with LLMs?
0:20 What are the techniques available?
1:24 Improve your model with prompt engineering!
2:12 RAG and Deep Memory!
3:04 Fine-tuning LLMs (LoRa and QLoRa).
5:41 Training from scratch.
8:20 Conclusion.
#ai #languagemodels #llm
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