Unified framework for building enterprise RAG pipelines with small, specialized models - llmware-ai/llmware
Small language models (SLMs) are compact, efficient, and don’t need massive servers—unlike their large language models (LLMs) counterparts. They’re built for speed and real-time performance and can run on our smartphones, tablets, or smartwatches. In this article, we’ll examine the top...
For years, the realm of language models was dominated by their giant counterparts – large language models (LLMs). With billions (even trillions) of parameters,LLMs boasted impressive capabilities, but their sheer size came at a cost: immense computational power, high storage needs, and limited...
LLMWare.ai, a pioneer in deploying and fine-tuning Small Language Models (SLMs) announced today the launching ofModel Depotin Hugging Face, one of the largest collections of SLMs that are optimized for Intel PCs. With over 100 models spanning multiple use cas...
LLMWare’s Model HQ is now available on Snapdragon X Series devices! With Model HQ, AI PCs powered by Snapdragon X Series processors can deliver powerful, secure, ready-to-use AI directly on their devices, including inference on the Qualcomm Hexagon NPU
The Phi 3 models stand out for their exceptional performance, surpassing both similar and larger-sized models in tasks involving language processing, coding, and mathematical reasoning. Notably, the Phi-3-mini, a 3.8 billion parameter model within this family, is available in versions that handle ...
For example, organic data from the Web could include the statement of a mathematical problem followed by the final solution, with the reasoning steps coming afterward. This makes it harder for an LLM to learn to generate the solution from the problem statement. In contrast, a synthetic ...
While those models will still be the gold standard for solving many types of complex tasks, Microsoft has been developing a series of small language models (SLMs) that offer many of the same capabilities found in LLMs but are smaller in size and are trained on smaller amounts of data. Th...
Small language models (SLMs), despite their widespread adoption in modern smart devices, have received significantly less academic attention compared to their large language model (LLM) counterparts, which are predominantly deployed in data centers and cloud environments. While researchers continue to ...
Paper tables with annotated results for A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness