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The Best Inference APIs for Open LLMs to Enhance Your AI App

Unite.AI

Imagine this: you have built an AI app with an incredible idea, but it struggles to deliver because running large language models (LLMs) feels like trying to host a concert with a cassette player. This is where inference APIs for open LLMs come in. The potential is there, but the performance? But which API should you use?

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Revolutionizing Fine-Tuned Small Language Model Deployments: Introducing Predibase’s Next-Gen Inference Engine

Marktechpost

Predibase announces the Predibase Inference Engine , their new infrastructure offering designed to be the best platform for serving fine-tuned small language models (SLMs). As AI becomes more entrenched in the fabric of enterprise operations, the challenges associated with deploying and scaling SLMs have grown increasingly daunting.

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Design Patterns in Python for AI and LLM Engineers: A Practical Guide

Unite.AI

As AI engineers, crafting clean, efficient, and maintainable code is critical, especially when building complex systems. For AI and large language model (LLM) engineers , design patterns help build robust, scalable, and maintainable systems that handle complex workflows efficiently. Strategy, Observer) 1.

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IoT-LLM: An AI Framework that Integrates IoT Sensor Data with LLMs to Enhance their Perception and Reasoning Abilities in the Physical World

Marktechpost

MARS Lab, NTU has devised an innovative IoT-LLM framework that combats the limitations of the LLM in handling real-world tasks. Rule-based systems, traditional machine learning models, and basic AI-driven methods are conventional models for processing IoT data. The IoT-LLM framework consists of these three steps: 1.

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Google AI Researchers Propose ‘MODEL SWARMS’: A Collaborative Search Algorithm to Flexibly Adapt Diverse LLM Experts to Wide-Ranging Purposes

Marktechpost

These limitations call for a methodology that can adapt LLMs efficiently without extensive tuning or restrictive assumptions, especially in low-data settings. This enables efficient adaptation without supervised fine-tuning, making it suitable for low-data contexts with as few as 200 examples.

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Stanford Researchers Propose LoLCATS: A Cutting Edge AI Method for Efficient LLM Linearization

Marktechpost

Researchers from Stanford University, Together AI, California Institute of Technology, and MIT introduced LoLCATS (Low-rank Linear Conversion via Attention Transfer). LoLCATS is a two-step method designed to efficiently improve the quality of linearized large language models without the need for expensive retraining on billions of tokens.

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Meet PowerInfer: A Fast Large Language Model (LLM) on a Single Consumer-Grade GPU that Speeds up Machine Learning Model Inference By 11 Times

Marktechpost

In a recent study, a team of researchers presented PowerInfer, an effective LLM inference system designed for local deployments using a single consumer-grade GPU. The team has shared that PowerInfer is a GPU-CPU hybrid inference engine that makes use of this understanding. Check out the Paper and Github.