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José David Baena
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Tiny Language Models
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Tiny Language Models

Master efficient LLMs under 3B parameters—models that match 5× larger competitors on reasoning tasks. From distillation to edge deployment, learn the techniques making AI accessible everywhere.

complete10 / 10 episodes•208 min total•advanced
Series Progress100%

What You'll Learn

  • ✓Understand model compression techniques (distillation, quantization, pruning)
  • ✓Implement efficient attention mechanisms (MQA, GQA, Flash Attention)
  • ✓Fine-tune tiny models for domain-specific tasks
  • ✓Deploy models to edge devices (mobile, IoT, embedded)
  • ✓Optimize inference for production environments

Episodes by Track

🏗️

Foundations & Architecture

Core concepts, mathematical foundations, and architectural patterns for tiny language models. Covers compression techniques, attention mechanisms, and model design.

5 posts
1

Tiny Language Models: How 1.3B Parameters Can Beat 7B on Reasoning

9/5/2025•34 min read
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2

Mathematical Foundations of Model Compression: Theory Behind Tiny LLMs

9/10/2025•12 min read
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3

Model Compression: 14GB to 450MB While Keeping 90% Quality

9/15/2025•26 min read
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4

Efficient Attention Mechanisms for Tiny Language Models

9/19/2025•26 min read
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5

Tiny LLM Architecture Comparison: TinyLlama vs Phi-2 vs Gemma vs MobileLLM

9/20/2025•23 min read
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Training & Optimization

Advanced training techniques including knowledge distillation, quantization-aware training, and domain-specific fine-tuning strategies.

3 posts
6

Knowledge Distillation: How to Train a 1.5B Model That Matches Your 7B

9/23/2025•20 min read
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7

Quantization-Aware Training: INT8/INT4 Models That Maintain Quality

9/27/2025•15 min read
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8

Fine-Tuning Tiny Models: LoRA, QLoRA, and Domain Adaptation Strategies

10/1/2025•18 min read
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Deployment & Production

Practical guides for deploying tiny models to edge devices and production environments with real-world case studies.

2 posts
9

Edge Device Deployment: Running Tiny LLMs on Raspberry Pi, Mobile, and IoT

10/4/2025•17 min read
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10

Tiny LLM Deployment Patterns: Architecture Blueprints from Published Benchmarks

10/13/2025•17 min read
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Prerequisites

  • •Python
  • •PyTorch
  • •Transformers
  • •Machine Learning Fundamentals

Who This Is For

  • •ml-engineers
  • •researchers
  • •ai-developers

Topics Covered

llmmachine-learningaiedge-computingoptimization