Small Language Models

Small Language Models

Phi, Qwen, Gemma; on-device inference; distillation; tool-call fine-tunes.

14Articles
14Topics covered
Articles in this category

All 14 articles, sorted alphabetically

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ARTICLE · 01

Distillation for Production Inference

Teacher-student in 2026: when distillation is worth it.

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ARTICLE · 02

Fine-Tuning Phi-4

QLoRA recipe for narrow tasks.

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ARTICLE · 03

On-Device AI: Running SLMs on Smartphones and the Death of Cloud Dependency

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ARTICLE · 04

On-Device LLM Inference

What runs on phones, laptops, and edge boxes in 2026.

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ARTICLE · 05

Phi, Qwen, Gemma: Small Models Compared

The 1B-9B parameter sweet spot in 2026.

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ARTICLE · 06

SLM Distillation Data Recipe

Teacher outputs filtering and CoT capture.

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ARTICLE · 07

Evaluating Small Models: Common Pitfalls

Why your small model looks worse than it is.

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ARTICLE · 08

Small Models for Tool Calling

When a 3B model beats GPT-4 for the agent's tool layer.

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ARTICLE · 09

SLMs on Mobile in 2026

iOS Foundation Models and Android Gemini Nano.

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ARTICLE · 10

SLMs in IoT: Giving 'Dumb' Appliances a Voice with Local 1B Parameter Models

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ARTICLE · 11

Small Models for Classification

Cheap inference for high-volume routing.

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ARTICLE · 12

Structured Output with Small Models

JSON schemas at 3B parameters.

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ARTICLE · 13

The Economics of SLMs: Why Startups Are Saving Millions by Switching to Smaller Footprints

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ARTICLE · 14

TinyLlama and the 1B Frontier: What Can You Actually Do with a 1-Billion Parameter Model?

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