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A small language model runs on ordinary hardware fast enough to serve one user, and an LLM is one that does not. SLM vs LLM compared, with 200 measured runs.
TL;DR: Large language models process text through tokenization, embeddings, transformers, and self-attention. They are trained through pre-training, fine-tuning, and alignment, the...
For the past few years, large language models have felt unstoppable... Every few months, a bigger model arrived. Longer context. Better fluency. Fewer hallucinations. More paramete...
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...
Large Language Models (LLMs) have significantly improved the way organizations build AI-powered applications. One of the most successful patterns is Retrieval-Augmented Generation...
Large language models can write essays, generate code, explain concepts, summarize documents, and hold conversations.Continue reading on Medium »
The race to build better large language models isn't slowing down, and this week Moonshot AI introduced another major milestone: Kimi K3. At first glance, the headline is impressi...
Recursive Language Models (RLMs) are a general inference paradigm that treats long prompts as part of an external environment and allows the LLM to programmatically examine, decomp...
Large Language Models (LLMs) are commonly accessed through cloud APIs provided by services such as OpenAI, Anthropic, or Google Gemini. However, there are many situations where dev...
TL;DR: LLM evaluation metrics are measurements used to assess the performance of large language models. They cover areas such as output quality, factual grounding, safety, and oper...
Fine-tuning large language models isn’t a one-size-fits-all exercise anymore. What started as a technique mostly discussed in AI research…Continue reading on Medium »
Why use it? How to implement it? What can we do when it fails? The post How to Implement Structured Output with Local LLMs appeared first on Towards Data Science.
Liquid AI released LFM2.5-2.6B, an agentic model that plans, calls tools, and completes multi-step tasks entirely on-device. The 2.69B parameter model pairs 22 double-gated short c...
This article is a summary of the research paper “Why Larger Language Models Do In-context Learning Differently?” by Zhenmei Shi, Junyi Wei…Continue reading on Medium »
LLM testing explained: types, key evaluation metrics, how to build a testing strategy, popular frameworks, common challenges, and real-world use cases.
Large Language Models are fundamentally stateless. If an application sends the question “What is my favorite programming language?” to a model, the model cannot automatically know...
Liquid AI released LFM2.5-VL-3B, a 3.1B-parameter vision-language model built for on-device deployment. It averages 80.7 on ScreenSpot-v2 and lifts RefCOCO grounding from 57.1 to 8...
The Uncomfortable Truth You’ve spent days prompt-engineering your LLM. You’ve benchmarked Claude against GPT. You’ve debated whether to use Mixtral. But your RAG pipeline is still...
Running a 70B model in production is expensive, and for many tasks, unnecessary. If you're building a focused pipeline, a well-trained 3B model will match or beat the 70B on your s...
On August 28, Tencent Hunyuan released and open-sourced Hy4 preview, its next-generation large language model with 770 billion total parameters and a context window exceeding 1 mil...
Z.ai has released GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series — a 320B-total / 18B-active MoE with a 1,048,576-token context window, MIT-licensed weights...
Software development is entering a new phase driven by Large Language Models (LLMs). These models allow developers to move beyond deterministic, rule-based systems and build applic...
But by keeping these limits in mind, and planning for them, we can effectively use these small, local models for the following broad operations scenarios.
Slava S. (slvDev) has optimized a 28.9M-parameter LLM running locally on an ESP32-S3 development board at around 9 tokens/s while generating text, or more exactly, telling short st...
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