LLMs

A collection of 26 posts

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.

llama

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

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.

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.

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.

llama

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.

LLMs

The Tiny LLM Revolution - Part 1

This article examines the emergence of Small Language Models (SLMs), discussing the impact of high-quality data on their capabilities. It highlights studies like TinyStories and Microsoft's Phi-series, exploring how SLMs can achieve performance comparable to larger models.