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marktechpost.com /3 weeks ago

Liquid AI Introduces LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M: Dense Bi-Encoder and Late-Interaction Models for Fas...

Liquid AI's LFM2.5 Retrievers combine a dense bi-encoder and ColBERT late-interaction model for multilingual search on edge devices. The post Liquid AI Introduces LFM2.5-Embedding-...

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towardsdatascience.com /1 week ago

Setting Up Your Own Large Language Model

Still a long way to go, but the future is promising The post Setting Up Your Own Large Language Model appeared first on Towards Data Science.

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medium.com /1 month ago

How Large Language Models Actually Work

Large Language Models ExplainedContinue reading on Medium »

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towardsdatascience.com /4 days ago

Long Context Isn’t Free — I Built a Safe Prompt-Pruning Layer That Makes LLM Systems Work

LLMs don’t fail because they forget—they fail because they remember too much. As conversations grow, prompts accumulate redundant and low-value tokens, driving up cost and latency...

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venturebeat.com /1 month ago

Context compression finally works in production: new research cuts LLM input 16x without the accuracy hit

Context windows are becoming a computational bottleneck. The longer an agent runs, the more tokens accumulate from retrieved documents, reasoning traces and conversation history, a...

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simplilearn.com /5 days ago

SLM vs LLM: Key Differences and Use Cases | Simplilearn

TL;DR: LLMs and SLMs are two types of language models used in artificial intelligence systems. LLMs are built to handle large-scale tasks with higher reasoning ability, while SLMs...

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marktechpost.com /1 month ago

NVIDIA AI Releases Nemotron-Labs-Diffusion: A Tri-Mode Language Model with 6× Tokens Per Forward Over Qwen3-8B

NVIDIA researchers have released Nemotron-Labs-Diffusion, a language model family that unifies three decoding modes in one architecture. The model supports autoregressive (AR) deco...

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machinelearningmastery.com /1 month ago

The Statistics of Token Selection: Logits, Temperature, and Top-P Walkthrough

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.

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dzone.com /2 weeks ago

A Low-Latency Routing Pattern for Multiple Small Language Models

A multi-SLM platform creates value only when specialization does not introduce a new latency tier. Small language models are inexpensive enough to dedicate to focused work such as ...

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venturebeat.com /1 month ago

MeMo's memory model lets teams upgrade their LLM without retraining it — and performance jumps 26%

Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context win...

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pandaily.com /1 month ago

MiniMax Prepares to Launch Next-Generation M3 Large Language Model

Chinese AI unicorn MiniMax is preparing to launch its M3 large language model featuring a custom sparse attention mechanism, claiming 9.7x prefilling speed improvements.

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speechtechmag.com /1 month ago

From Large Language Models to Conversational Awareness

Why enterprise voice AI must learn to understand human interaction

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graphicdesignjunction.com /1 month ago

Small Language Models vs Large LLMs: Performance, Cost, and Latency Compared

AI tools are now part of daily work for designers, writers, developers, students, and business teams. Some people use large AI models like OpenAI ChatGPT, while others use smaller...

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venturebeat.com /1 month ago

Google's DiffusionGemma generates 256 tokens in parallel and self-corrects as it goes

GenAI image generators like Stable Diffusion do not draw a picture pixel by pixel from left to right. They start with noise and iteratively refine the entire image in parallel unti...

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salesforce.com /1 month ago

Can Language Models Remember What They Learn?

Post-training methods (RLVR, On-policy distillation) are Episode-local Language models are getting better at learning from feedback during post-training. In reinforcement learning...

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machinelearningmastery.com /3 weeks ago

Clustering Unstructured Text with LLM Embeddings and HDBSCAN

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...

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adexchanger.com /3 weeks ago

Large Language Models Are Overkill For Some Marketing Tasks. Enter The Small Language Model

It’s no secret that large language models (LLMs) have gotten exorbitantly expensive. Companies are starting to limit their employees’ AI usage to save money; OpenAI has even discus...

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kdnuggets.com /1 week ago

5 Ways Small Language Models Are Powering Next-Gen Agents

This article looks at five concrete ways SLMs are showing up inside next-generation agents right now, from the research backing them to the tools and numbers worth knowing if you'r...

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kdnuggets.com /1 week ago

5 Ways Small Language Models Are Powering Next-Gen Agents

This article looks at five concrete ways SLMs are showing up inside next-generation agents right now, from the research backing them to the tools and numbers worth knowing if you'r...

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theregister.com /1 month ago

Google's new open-weights model brings image-generation tricks to AI text generation

Language model builds on diffusion tech to boost output performance by up to 4x, claims Chocolate Factory

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webwire.com /2 weeks ago

World's First Commercial Multimodal LLM for Cultural Tourism Enters Broad Application

The world's first commercial multimodal large language model (LLM) for cultural tourism, called BoGuan, has entered broad application in Xi'an, China. This model generates commerci...

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medium.com /1 week ago

Day 2: Understanding Large Language Models (LLMs)

After learning the basics of Generative AI on Day 1, it’s time to explore one of the most important technologies behind it: Large Language…Continue reading on Medium »

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marktechpost.com /1 month ago

Liquid AI Releases LFM2.5-8B-A1B: An On-Device MoE Model With 8.3B Total and 1.5B Active Parameters

Liquid AI's LFM2.5-8B-A1B activates 1.5B of 8.3B parameters, offering 128K context, reasoning, and tool calling on consumer hardware. The post Liquid AI Releases LFM2.5-8B-A1B: An...

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venturebeat.com /2 weeks ago

New agentic memory framework uses 118K tokens per query. LangMem burns through 3.26M.

Long-horizon reasoning exposes a core weakness in AI agents: context windows fill up fast, and retrieval pipelines return noise instead of signal.To solve this, researchers at the...

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