LLMs

Fine-Tuning & Small Language Models

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.

LLMs

Multilingual LLMs: Progress, Challenges, and Future Directions

Multilingual LLMs face challenges like cross-lingual knowledge barriers, data imbalances, and performance disparities in low-resource languages. Key advancements include multilingual fine-tuning, retrieval-augmented generation (RAG), and adaptive architectures.

LLMs

Are Agentic Frameworks an Overkill?

Agentic frameworks offer advanced adaptability and automation but come with high complexity and cost. This article explores their benefits, limitations, and practical alternatives to help you decide whether they are the right solution for your AI and automation needs.

LLMs

Edge Deployment of Language Models: Are They Ready?

Edge deployment of LLMs promises low latency, privacy, and real-time insights. This article explores the challenges, cutting-edge solutions, and future opportunities to make edge-based AI a reality across industries like healthcare, robotics, and IoT

LLMs

Is the current AI agents ecosystem again a Hype?

The AI agent ecosystem is rapidly evolving, blending autonomy and adaptability to transform industries. This analysis delves into their development, real-world applications, and challenges, highlighting advancements in decision-making, efficiency, and integration.

LLMs

LLMs Evaluation: Benchmarks, Challenges, and Future Trends

The evaluation of Large Language Models (LLMs) focuses on benchmarks, scalability, ethical challenges, and multimodal testing. Dynamic frameworks and emerging trends drive robust, adaptive AI performance, ensuring safer, efficient deployment in sensitive fields like healthcare, finance, and law.

LLMs

LLM Observability: Practices, Tools, and Trends

Explore LLM observability with this comprehensive guide. Understand metrics, logs, traces, and tools like Langfuse and SigNoz. Learn best practices, handle production challenges, and stay ahead with trends like multi-modal monitoring and AI-driven anomaly detection.

RAGs

RAG vs Long-Context LLMs: Approaches for Real-World Applications

This article compares Retrieval-Augmented Generation (RAG) with Long-Context Large Language Models (LLMs) in managing extensive data and complex queries, highlighting key technical differences and applications.

LLMs

Large Language Models for Next-Generation Recommendation Systems

Large Language Models (LLMs) transform recommendation systems by addressing challenges like domain-specific limitations, cold-start issues, and explainability gaps. They enable personalized, explainable, and conversational recommendations through zero-shot learning and open-domain knowledge.

Prem-1B-SQL: Fully Local Performant SLM for Text to SQL

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

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.

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

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

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.

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

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.

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.

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.

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.

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).