LLMs
Open-source LLMs like Llama 3.1 and Prem-1B-SQL offer affordability, flexibility, and customization, rivaling closed-source models like GPT-4o in performance. With enhanced transparency and control, they are ideal for businesses seeking scalable and innovative AI solutions tailored to their needs
LLMs
Large Language Models (LLMs) are transforming OCR systems by improving text recognition accuracy, enabling better multilingual support, and seamlessly integrating vision and language understanding to tackle complex tasks like scene text recognition and handwritten content.
LLMs
The era of GPT-4's dominance is ending as diverse, specialized language models take the stage. Companies now favor both open-source and proprietary models tailored to specific tasks, moving away from one-size-fits-all solutions. Small Language Models (SLMs) offer efficiency for niche needs.
llama
Text to SQL is a task that aims to convert natural language questions into corresponding SQL queries that can be executed in relational databases. Interestingly, this problem statement has been through research before the rise of Large Language Models.
LLMs
The alignment of generative models with human feedback has significantly improved the performance of natural language generation tasks. For large language models (LLMs), alignment methods like reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO) have consistently worked better than just supervised fine-tuning (SFT) alone based on current
LLMs
Our study aims to explore how different providers use different techniques in order to serve LLMs influencing the overall quality of the models. To achieve this, we conduct empirical tests using various prompts, allowing us to systematically compare the outcomes across various service providers. This approach helps us identify significant
LLMs
Large Language Models (LLMs) are in the spotlight, mainly due to their emerging capabilities. Please refer to our previous blog post on Emergent abilities in LLMs for a deeper dive into this topic. As the old saying goes, You can't make a silk purse out of a sow&
LLMs
Emergence, a concept well-studied in fields like physics, biology, and mathematics, describes how complex systems can exhibit new, unforeseen properties as their complexity increases. Inspired by P.W. Anderson's influential idea that "More is Different," suggesting that complexity can lead to unexpected behaviors not evident from
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