LLMs Multimodal LLMs: Architecture, Techniques, and Use Cases Multimodal Large Language Models (LLMs) integrate diverse data types—text, images, audio, and video—into unified frameworks, enabling advanced applications like image captioning, document analysis, and healthcare solutions.
News Featured Lyra Drake's Public Debut at Art Basel 2024 – A New Frontier in AI and Art At Art Basel 2024 in Miami Beach, Lyra Drake, a groundbreaking multidisciplinary artist, debuts her first major exhibition, Infinite Faith in a Finite World. This exhibit marks a transformative moment in the intersection of art and technology, powered by PREM AI's innovative AI solutions.
Introducing Prem-1B Prem AI introduces Prem-1B, an open-source Small Language Model built for Retrieval-Augmented Generation (RAG) tasks. Based on a decoder-only transformer architecture, it supports up to 8192 tokens. The model is available on Hugging Face under Apache 2.0.
Open Source Release: Ayup: Facing the Deployment Nightmare 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?
LLMs Are Open-Source Models Good Now? 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 How LLMs Are Transforming OCR for the Next Generation 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.
RAGs Advanced RAG Methods: Simple, Hybrid, Agentic, Graph Explained Discover Retrieval-Augmented Generation (RAG) methods: Simple RAG for basic tasks, Hybrid RAG combining retrieval techniques, AgenticRAG with modular multi-agent systems, and GraphRAG leveraging graph data. Each method offers unique strengths, tailored for tasks.
AI Agents Beginners Guide AI agents are autonomous systems that perceive environments, make decisions, and learn over time. They range from simple reflex agents to advanced generative models. With applications in automation, creativity, and strategy, they enhance efficiency but face challenges like bias and data privacy
Anybody can be a Solopreneur in 2024, thanks to Generative AI 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.
Prem Articles Transformer Inference: Techniques for Faster AI Models 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.
News Devin: accelerating developers but not replacing them AI tools like GitHub Copilot and Devin enhance developer productivity by automating repetitive tasks and offering coding assistance. However, they fall short in creativity, contextual understanding, and complex decision-making, ensuring developers remain indispensable.
Prem Articles Open-Source Code Language Models: DeepSeek, Qwen, and Beyond Open-source CodeLLMs like DeepSeek-Coder and Qwen2.5-Coder revolutionize code intelligence by offering repository-level training, multilingual support, and advanced features. These models rival proprietary solutions, fostering transparency, collaboration, and customization.
News Generative AI Adoption: Industry Impact, Challenges, and Future Trends Generative AI is revolutionizing industries, reshaping how businesses operate and innovate. With rapid adoption and increased investments, organisations are leveraging AI for tasks like content creation, customer support, and software development.
Prem Articles Chunking Strategies in Retrieval-Augmented Generation (RAG) Systems 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 vs. NLU: the Real Power of Chatbots 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.
Prem Articles AI Sustainability: Reducing Carbon Footprint and Driving Innovation As AI technology expands, so does its carbon footprint. This underscores the challenge: while AI accelerates digital transformation, it risks becoming a significant contributor to global CO₂ emissions.
Prem Articles Generative AI Integration for Web Developers Generative AI offers new opportunities in content creation, but challenges like model selection and data complexity hinder developers. This article explores these issues and introduces Prem AI, a platform designed to simplify AI integration.
llama State of Text2SQL 2024 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.
News Announcing our $14M Strategic Seed Round Prem has secured a $14M strategic seed round to expand its AI ecosystem. With backing from investors like David Maisel, Prem aims to empower businesses with generative AI while ensuring data ownership. Key offerings include the Prem Platform and Autonomous Fine-tuning Agent for custom AI models.
Introducing Prem-Operator, An Open-Source Kubernetes Operator for AI/ML Today, we are excited to announce the open-source release of the “Prem-Operator,” A Kubernetes operator that eases the deployment of AI and ML workloads, with an initial focus on inference. The launch of the Prem Operator marks a major advancement in our goal to provide AI that you can fully
Introducing Benchmarks v2 Prem's Benchmarks v2 is an open-source project evaluating 13+ LLM inference engines, including vLLM and TensorRT LLM, across precisions like float32, float16, int4, and int8. It helps the open-source community and enterprises understand LLM inference performance.
RAGs RAG Strategies The article "RAG Strategies" explores Retrieval-Augmented Generation (RAG) methods, detailing Naive RAG, Advanced RAG, and Modular RAG approaches. It introduces RAFT, a fine-tuning technique, and discusses optimizing large language models for RAG tasks.
Finetuning with LoRA and variants The article "Finetuning with LoRA and Variants" discusses Low-Rank Adaptation (LoRA), a technique that efficiently fine-tunes large language models by adding a small number of trainable parameters, reducing computational costs. It also explores LoRA+ and other innovations enhancing model adaptation.
Mixture of Experts - Part 2 The article "Mixture of Experts - Part 2" explores the resurgence of Mixture of Experts (MoE) architectures in NLP, highlighting models like Mixtral 8x7B and DeepMind's Mixture-of-Depths. It discusses MoE's evolution since 1991, focusing on scalability and efficiency in large language models.
LLMs Model Alignment Process The article discusses methods to align large language models (LLMs) with human preferences, focusing on techniques like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). It also introduces non-RL methods such as Kahneman-Tversky Optimization (KTO).
