LLMs
In this second part of the series, we delve into how LLMs can serve as evaluators, a concept referred to as "LLM as judge." Building on the previous blog, which introduced early benchmarks and metrics for evaluating large language models (LLMs) and highlighted issues with these evaluation methods,
LLMs
The rapid development of Large Language Models (LLMs) also necessitates advancing robust evaluation strategies and systems. For a broader understanding of these technological advancements, look at our previous exploration in The Tiny LLM Revolution. In this blog post, we do a deep dive into existing evaluation benchmarks and discuss future
llama
The previous blog post describes how Small Language Models (a.k.a SLMs) hold potential in the generative AI space. We discussed the emergence of the idea of SLMs and how synthetic data can be a driving factor in producing powerful small language models (for example: Phi-2 by Microsoft). Just
LLMs
Large Language Models (LLMs) became very famous because of the rise of their emergent capabilities and the concept of “one model does it all”, where a single model can do all sorts of tasks like generating emails, and poems, summarizing long papers, generating code, etc. However, LLMs are also infamous
News
Very recently llm360.ai has released LLM360, one of the very first truly open-source models. Popular open-source models, such as Llama2, and Mistral, are not fully open-source. Only the model's weights are released. But just making the weights public does not solve all the problems. Training an LLM