There are two main approaches to improving the performance of large language models (LLMs) on specific tasks: finetuning and retrieval-based generation. Finetuning involves updating the weights of an LLM that has been pre-trained on a large corpus of text and code.
The misconception of self-learning capabilities of Large Language
Pramit Saha on LinkedIn: #transformers #infosystechcohere
Finetuning LLM
How to develop a Enterprise grade LLM Model & Build a LLM Application
Retrieval Augmented Generation for Clinical Decision Support with
Real-World AI: LLM Tokenization - Chunking, not Clunking
What is the future for data scientists in a world of LLMs and
Issue 24: The Algorithms behind the magic
Finetuning LLM
Issue 13: LLM Benchmarking
How to develop a Enterprise grade LLM Model & Build a LLM Application
50 excellent ChatGPT prompts specifically tailored for programmers
The Power of Embeddings in SEO 🚀
Real-World AI: LLM Tokenization - Chunking, not Clunking