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small-language-model

Small Language Models (SLMs) are compact versions of traditional Large Language Models (LLMs), designed to operate efficiently on standard hardware. Typically containing a few million to a few billion parameters, SLMs are tailored for specific tasks, making them faster and more cost-effective. They are particularly suitable for deployment on mobile devices and edge computing environments, allowing for real-time applications without the need for extensive computational resources. As businesses increasingly seek efficient AI solutions, SLMs represent a significant shift towards more agile and focused approaches in natural language processing and machine learning.

Small Language Models

Towards AI

If you are not a Medium member, you can read this article here . Large language models have become very popular recently due to the amazing capabilities shown by these models. Their applicability to a...

Why Small Language Models Make Business Sense

Towards AI

Image generated by Gemini AI Small Language Models are changing the way businesses implement AI by providing solutions that operate efficiently using standard hardware. Despite the attention given to ...

It is raining Language Models! All about the new Small Language Models — Phi-2

Towards AI

It is raining Language Models! All about the new Small Language Model— Phi-2 The Dawn of Small Language Models: Introducing Phi-2 that outperformed Llama-2(70B), which is 25 times its size! Image by ...

Your Company Needs Small Language Models

Towards Data Science

Image generated by Stable Diffusion When specialized models outperform general-purpose models “Bigger is always better” — this principle is deeply rooted in the AI world. Every month, larger models ar...

Small Language Models (SLMs) in Enterprise: A Focused Approach to AI

Towards AI

One size does not fit all. Large language models (LLMs) like GPT-4 have certainly grabbed headlines with their broad knowledge and versatility. Yet, there’s a growing sense that sometimes, bigger isn...

Some Technical Notes About Phi-3: Microsoft’s Marquee Small Language Model

Towards AI

The model ius able to outperform much larger alternatives and now run locally on mobile devices. Created Using Ideogram I recently started an AI-focused educational newsletter, that already has over ...

Not-So-Large Language Models: Good Data Overthrows the Goliath

Towards Data Science

(Image generated by DALL·E) How to make a million-sized language model that tops a billion-size one In this article, we will see how Language Models (LM) can focus on better data and training strategi...

Small But Mighty — The Rise of Small Language Models

Towards Data Science

Our world has been strongly impacted by the launch of Large Language Models (LLMs). They exploded onto the scene, with GPT-3.5 amassing a million users in a single app in just five days — a testament ...

Exploring “Small” Vision-Language Models with TinyGPT-V

Towards Data Science

TinyGPT-V is a “small” vision-language model that can run on a single GPU Summary AI technologies are continuing to become embedded in our everyday lives. One application of AI includes going multi-m...

Large Language Models: DistilBERT — Smaller, Faster, Cheaper and Lighter

Towards Data Science

Large Language Models: DistilBERT — Smaller, Faster, Cheaper and Lighter Unlocking the secrets of BERT compression: a student-teacher framework for maximum efficiency Introduction In recent years, th...

Can We Use Multiple Small Language Models Instead of Large Language Models?

Level Up Coding

Natural language processing (NLP) has been primarily driven by large language models (LLMs) like GPT-4, known for their impressive capabilities in understanding and generating text. These LLMs have de...

Language Models

Dive intro Deep Learning Book

In Section 9.2 , we see how to map text sequences into tokens, where these tokens can be viewed as a sequence of discrete observations, such as words or characters. Assume that the tokens in a text se...