LLMs How to Succeed with Custom Reasoning Models? Explore key strategies to successfully develop and optimize custom reasoning models. Learn how explicit reasoning structures, reinforcement learning techniques, advanced evaluation metrics, and optimized deployment enhance AI's logical inference and problem-solving capabilities.
LLMs SLM vs LoRA LLM: Edge Deployment and Fine-Tuning Compared A concise overview comparing the advantages and limitations of full fine-tuning Small Language Models (SLMs) versus LoRA-based fine-tuning of Large Language Models (LLMs). The article covers inference efficiency, quantization methods, robustness and deployment strategies on constrained hardware
LLMs Open Source Reasoning Models: Welcome Qwen QwQ and QvQ Open-source reasoning models Qwen QwQ and QvQ represent a shift in AI from generation to structured reasoning. With transparency, multimodal capabilities, and fine-tuned performance, they set benchmarks in logical problem-solving, advancing industries like finance, healthcare, and education.
LLMs Open Source Audio Models: Text-to-Speech and Speech-to-Text Open-source frameworks like BASE TTS, ESPnet, and FunASR are transforming Text-to-Speech and Speech-to-Text technologies in 2025. With advancements in scalability, natural prosody, and low-resource deployment, these tools make high-quality speech AI accessible and customizable globally.
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 Balancing LLM Costs and Performance: A Guide to Smart Deployment Balancing LLM costs and performance requires strategies like dynamic model routing, hybrid deployment, and fine-tuning smaller models for specific tasks. Techniques such as token optimisation, caching, and leveraging open-source models help reduce expenses while maintaining efficiency.
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 What is a MicroSaaS and How to Get Started with Making One Using AI AI is transforming MicroSaaS by automating development, enabling personalization, and optimizing scaling. Tools like PREM-AI help developers create scalable, niche-focused solutions faster and with less effort. The future includes AI-native workflows and hyper-personalized features
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.
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.
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.
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.
LLMs 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.
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).
LLMs Providers Empirical Testing Prem's study evaluates how different providers implement Large Language Models (LLMs), affecting model quality. Using various prompts, the study compares providers like Anyscale, Replicate, FireworksAI, and Together, assessing parameters such as verbosity, correctness, and processing speed
LLMs LLM Datasets and Contamination Prem AI's post addresses dataset contamination in LLM training, where overlap between training and test sets inflates performance. It explores detection methods, data curation best practices, and ethical concerns around mislabeled or duplicated content.
LLMs Emergent Capabilities of LLMs ChatGPT ha detto: ChatGPT The article explores how LLMs develop emergent abilities like few-shot prompting and chain-of-thought reasoning as they scale, highlighting key research findings and debates on predictability, potential misuse, and understanding their limitations.
LLMs Evaluation of LLMs - Part 2 The article explores using large language models (LLMs) as evaluators, addressing concerns about accuracy and inherent biases. It highlights the need for scalable meta-evaluation schemes and discusses fine-tuned evaluation models like Prometheus 13B, which aligns closely with human evaluators.
LLMs Evaluation of LLMs - Part 1 The article "Evaluation of LLMs - Part 1" delves into the rapid development of Large Language Models (LLMs) and the necessity for robust evaluation strategies. It examines traditional n-gram-based metrics like BLEU and ROUGE, discussing their roles and limitations in assessing LLM performance.
LLMs Mamba Simplified - Part 1 - Essential Pre-Requisites This article introduces foundational concepts like derivatives, differential equations, and neural networks, essential for understanding Mamba—a Small Language Model based on State Space Models (SSMs). It serves as a refresher to grasp Mamba's architecture effectively.
