Module 1: The Open Source LLM Ecosystem
The open source LLM ecosystem in 2024-2025 looks nothing like it did in 2022. In 2022, GPT-3 had no open source competitor. In 2025, Llama 3.3 70B matches GPT-4 on most benchmarks. Qwen 2.5 72B exceeds it on coding tasks. Mistral Large competes with Claude 3 Opus. The gap between open and closed models has collapsed across most practical tasks.
This module maps the ecosystem so you can navigate it effectively. Not every model summary you will ever need - that changes weekly - but the frameworks for understanding what matters: architecture choices, training data, capability profiles, and licensing.
The Model Families
The open source ecosystem is organized around a handful of foundational model families, each with its own architecture choices and capability profile:
Llama family (Meta) - The most widely used open source LLM family. Llama 3.1 and 3.3 are genuine GPT-4 competitors for most tasks. Strong general purpose. 8B, 70B, and 405B parameter sizes. Apache 2.0 license (with Meta's usage policy overlay).
Mistral family (Mistral AI) - High quality per parameter. Mistral 7B punched above its weight class on release. Mixtral 8x7B introduced MoE to the open source world. Good coding and reasoning. Apache 2.0 license.
Qwen family (Alibaba) - Exceptional on coding (Qwen2.5-Coder), math (Qwen2.5-Math), and multilingual tasks. 0.5B to 72B+ range. Increasingly competitive at the top end. Apache 2.0 license.
Gemma family (Google) - Distilled from Gemini. Compact, efficient, strong for their size. Gemma 2 2B/9B/27B. Gemma license (permissive but not Apache 2.0).
Phi family (Microsoft) - Small models with disproportionate capability. Phi-3.5 Mini (3.8B) beats many 7B models. Heavily instruction-tuned. MIT license.
Model Capability Landscape
Lessons in This Module
| # | Lesson | Key Concept |
|---|---|---|
| 1 | State of Open Source LLMs | Current landscape, capability parity with closed models |
| 2 | Llama Family Deep Dive | Architecture, training, versions 1-3, capability profile |
| 3 | Mistral and Mixtral | Sliding window attention, MoE architecture, use cases |
| 4 | Qwen, Phi, and Gemma | Smaller families, coding strength, efficiency |
| 5 | Model Cards and Evaluation | Reading model cards, benchmark interpretation, red flags |
| 6 | Hugging Face Hub Ecosystem | Hub navigation, model discovery, tokenizer compatibility |
| 7 | Choosing the Right Model | Decision framework: task, hardware, license, latency |
| 8 | Open vs Proprietary Tradeoffs | Cost, control, capability, privacy, customization |
Key Concepts You Will Master
- Model naming conventions - what "instruct," "chat," "base," "GGUF," "AWQ" mean in model names
- Benchmark reading - what MMLU, HumanEval, GSM8K, and MT-Bench actually measure
- License implications - which open source licenses allow commercial use and how
- Parameter efficiency - why a 7B model at Q4 might outperform a 13B at Q2 for your use case
- Model families vs individual checkpoints - how to track evolving model families
Prerequisites
- Basic LLM understanding - LLMs Track
- Python familiarity