#large-language-models

#large-language-models

Large Language Models (LLMs) enable businesses to create tailored applications, but self-hosting them raises complexity—especially regarding privacy and control.

Prior reservation wastes memory even if the context lengths are known in advance, demonstrating the inefficiencies in current KV-cache allocation strategies in production systems.

We adopt the most widely studied models, popular programming languages, and datasets in our research to mitigate validity threats related to our findings.

The performance of closed-source LLMs typically exceeds that of open-source models in key metrics, emphasizing the importance of training data quality and model architecture.

The project developed by designer Jakub Koźniewski references the literary constraints and structure of the OuLiPo movement, applying these principles through contemporary digital and mechanical means.

The sky isn't falling. It's shifting.

As my method of work is all about intertwining physical with digital, I decided to literally build physical models that convey synthetic thoughts modeling my current state of mind.

Retrieval-Augmented Generation (RAG) techniques enhance LLMs by integrating external knowledge sources, which improves their performance in tasks requiring up-to-date or specialized information.

RAG transforms how we interact with large language models by enabling focused, relevant retrieval rather than feeding them entire documents, leading to more accurate responses.

The advancements in deploying smaller-scale Large Language Models (LLMs) on edge devices face challenges like memory limitations but initiatives like MLC LLM allow compatibility across various hardware.