The current era of Generative AI seems to primarily focus on chat interfaces and prompts, but the range of applications of large language models , or LLMs for short, is not limited to just that.
Traditional machine learning pipelines for predictive tasks like text classification usually rely on extracting structured, numerical features from raw text — for instance, TF-IDF frequencies or token embeddings — to feed into classical models such as logistic regression, ensembles, or support vector machines.
Text classification typically boils down to scenarios where a product review is "positive" or "negative", or a customer inquiry belongs to one category or another.
This article will teach you how to perform a language task like text classification by integrating locally hosted large language models (LLMs) of manageable size, like Mistral, Gemma, and Llama 3: all for free thanks to Ollama — a free repository for local LLMs — and the Scikit-LLM Python library.
In recent years, generative AI models like LLMs (large language models) have gradually taken over classical machine learning ones for addressing certain tasks, for instance, text classification .
This article is divided into four parts; they are: • The Problem with Static Batching • Code Example of Static Batching • Continuous Batching: Dynamic Scheduling and Ragged Batching • Full Implementation The simplest way to serve multiple requests together is to use static batching, by grouping them into fixed-size batches and processing each batch together.
When large language models, or LLMs for short, produce outputs, several criteria are at stake, including not only overall response relevance but also coherence and creativity.
Implementing hybrid search strategies is a critical step in building modern RAG (Retrieval-Augmented Generation) systems , especially when shifting from prototype to production-ready solutions.
Agentic loops in production can be synonymous with high costs, especially when it comes to both LLM and external application usage via APIs, where billing is often closely related to token usage.
TurboQuant has recently been launched by Google as a novel algorithmic suite and library for applying advanced quantization and compression to large language models (LLMs) and vector search engines — an indispensable element of RAG systems.
If you've ever watched two agents confidently write to the same resource at the same time and produce something that makes zero sense, you already know what a race condition feels like in practice.
This article is divided into three parts; they are: • How Attention Works During Prefill • The Decode Phase of LLM Inference • KV Cache: How to Make Decode More Efficient Consider the prompt: Today’s weather is so .
Creating an AI agent for tasks like analyzing and processing documents autonomously used to require hours of near-endless configuration, code orchestration, and deployment battles.
In the modern AI landscape, an agent loop is a cyclic, repeatable, and continuous process whereby an entity called an AI agent — with a certain degree of autonomy — works toward a goal.
Unlike fully structured tabular data, preparing text data for machine learning models typically entails tasks like tokenization, embeddings, or sentiment analysis.
