Small Language Models
Small Language Models
Master efficient AI with models that run anywhere - from laptops to edge devices
Small Language Models
Build production-ready applications with efficient, deployable language models that don't require expensive cloud infrastructure.
Why Small Language Models?
Small Language Models (SLMs) like Phi-3, Gemma, Qwen, and Llama 3.2 are revolutionizing AI deployment:
| Advantage | Description |
|---|---|
| Privacy | Run entirely on-device, no data leaves your infrastructure |
| Cost | Eliminate API costs with self-hosted inference |
| Latency | Sub-100ms responses without network roundtrips |
| Offline | Work without internet connectivity |
| Customization | Fine-tune for your specific domain |
Learning Path
┌─────────────────────────────────────────────────────────────────────────────┐
│ SLM LEARNING PATH │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ BASIC │ │
│ │ Local Setup ──────► Text Tasks ──────► Benchmarking │ │
│ └──────────────────────────────────┬──────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ INTERMEDIATE │ │
│ │ │ │
│ │ Fine-tuning ──► SLM + RAG ──► Edge Deploy ──► SLM Agents │ │
│ │ │ │
│ └──────────────────────────────────┬──────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ ADVANCED │ │
│ │ │ │
│ │ Train from Scratch ──► Speculative Decoding ──► Production │ │
│ │ │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘Projects Overview
Basic Projects
| Project | Time | Description |
|---|---|---|
| Local SLM Setup | ~2 hours | Run Phi-3, Gemma, Qwen locally with Ollama and llama.cpp |
| SLM for Text Tasks | ~2 hours | Classification, extraction, NER with small models |
| SLM Benchmarking | ~3 hours | Evaluate and compare different SLMs |
Intermediate Projects
| Project | Time | Description |
|---|---|---|
| SLM Fine-tuning | ~6 hours | Domain adaptation with Unsloth and QLoRA |
| SLM-Powered RAG | ~6 hours | Efficient RAG pipelines with local models |
| Edge Deployment | ~8 hours | Deploy to mobile, Raspberry Pi, and browsers |
| SLM Agents | ~6 hours | Build agents with function calling |
Advanced Projects
| Project | Time | Description |
|---|---|---|
| Training SLM from Scratch | ~5 days | Pre-train your own small language model |
| Speculative Decoding | ~4 days | Accelerate LLMs using SLMs as draft models |
| Production SLM System | ~5 days | Enterprise-grade SLM deployment |
Popular Small Language Models
| Model | Parameters | Strengths | Best For |
|---|---|---|---|
| Phi-3 | 3.8B | Reasoning, coding | General tasks |
| Gemma 2 | 2B, 9B | Instruction following | Chat, assistants |
| Qwen 2.5 | 0.5B-7B | Multilingual, math | Diverse applications |
| Llama 3.2 | 1B, 3B | On-device optimized | Mobile, edge |
| SmolLM | 135M-1.7B | Ultra-compact | Embedded systems |
Case Studies
Real-world implementations showing SLMs in production deployments.
| Case Study | Industry | Description | Status |
|---|---|---|---|
| Edge AI Customer Service | Customer Service | Privacy-first on-device customer support | Available |
| On-Device Medical Scribe | Healthcare | Local speech-to-text, SOAP notes, ICD-10 coding, FHIR export | Available |
| Emergency Triage Assistant | Healthcare | Offline ESI classification with rule-based red flag detection | Available |
Prerequisites
Before starting, you should have:
- Python 3.10+ and PyTorch experience
- Basic understanding of transformers
- Familiarity with command line tools
- 8GB+ RAM (16GB recommended for fine-tuning)