PREM and AWS have joined forces to deliver an accelerated Generative AI developer experience on both AWS and the PREM platform. To kick off this milestone, PREM co-organized a GenAI Hackathon with AWS in Barcelona, where AWS's ProServe team collaborated with PREM's team to tackle complex
With great enthusiasm, we unveil the Prem-1B series, an open-source, multipurpose large language model developed by Prem AI. This cutting-edge SLM offers the open community and enterprises the opportunity to harness capabilities that were once exclusively available through closed-model APIs, empowering them to build their advanced language models. The weights
Last week, we open-sourced PremSQL, a local first library that created customized Text-to-SQL solutions. When deploying RAG-based services (whether on documents or databases) for enterprises, it becomes crucial that the underlined data is not exposed to third-party APIs. With PremSQL, you can use our ready-made pipelines or customize and create
So you are given some Python project which creates an API endpoint which does some AI/ML inference. You need to run this on your own infra, a cloud VM or whatever. What do you do?
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
Machine learning and generative AI offer potential, but high costs and complexity challenge ROI. This article explores strategic integration to maximize value, with Prem AI providing the tools to bridge the gap.
Generative AI empowers solopreneurs to launch and scale businesses with unprecedented speed and creativity, breaking barriers like coding expertise or capital needs. By leveraging AI tools, individuals can ideate, iterate, and execute efficiently, turning solo ventures into agile enterprises.
Transformer inference powers tasks in NLP and vision, but is computationally intense, requiring optimizations. Large models like GPT-3 need extensive memory and FLOPs, with techniques like KV caching, quantization, and parallelism reducing costs.
Fine-tuning embeddings are crucial for enhancing domain-specific NLP applications. General models may fall short in specialised fields like healthcare or law. By fine-tuning, models improve accuracy, relevance, and understanding of specific terminologies, ensuring better performance in niche tasks.
Chunking enhances Retrieval-Augmented Generation (RAG) by splitting large texts into manageable parts for efficient processing in language models. This technique supports accurate responses, maintains context, and enables fast, parallel processing.
Function calling is seen as revolutionary in chatbots, enhancing real-time capabilities. However, older NLU + Core bots handled many use cases effectively. This article explores the evolution of chatbots, weighing the hype of function calling against the proven efficiency of traditional methods.
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.