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Tiny Language Models
Master the art of building efficient, production-ready LLMs under 3B parameters. From architectural foundations to edge deployment, learn how to achieve 80% of GPT-4 capability at 1% of the cost.
active0 / 10 episodes•0 min total•advanced
Series Progress0%
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
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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: The Complete Guide to Small, Efficient LLMs (2025)
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2
Mathematical Foundations of Model Compression: Theory Behind Tiny LLMs
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3
Model Compression Techniques: Complete Guide to Efficient LLMs
🔒Under Development
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4
Efficient Attention Mechanisms for Tiny Language Models
🔒Under Development
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5
Tiny LLM Architecture Comparison: TinyLlama vs Phi-2 vs Gemma vs MobileLLM
🔒Under Development
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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 Complete Tutorial: Train Tiny Models from Scratch
🔒Under Development
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7
Quantization-Aware Training: INT8/INT4 Models That Maintain Quality
🔒Under Development
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8
Fine-Tuning Tiny Models: LoRA, QLoRA, and Domain Adaptation Strategies
🔒Under Development
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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
🔒Under Development
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10
Tiny LLM Case Studies: Real-World Production Deployments with Metrics
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Prerequisites
- •Python
- •PyTorch
- •Transformers
- •Machine Learning Fundamentals
Who This Is For
- •ml-engineers
- •researchers
- •ai-developers