Open Source Models
The open source model ecosystem has changed what is possible for engineers without a nine-figure training budget. A Llama 3.1 70B running on-premise can match GPT-4 on most enterprise tasks. A fine-tuned Mistral 7B on your proprietary data can outperform a generic GPT-4 on your specific use case. The tooling - vLLM, Ollama, Unsloth, Axolotl - is production-grade and actively maintained.
The knowledge gap is not in the models. It is in how to work with them.
Why Open Source Models Now
Three things changed simultaneously:
Model quality. The gap between open and closed models has collapsed. Qwen 2.5 72B, Llama 3.3 70B, and Mistral Large are competitive with GPT-4 on most benchmarks. For domain-specific tasks with fine-tuning, open models frequently win.
Tooling maturity. vLLM handles continuous batching, PagedAttention, and LoRA adapter serving. Unsloth makes LoRA fine-tuning 2x faster with 70% less memory. Ollama makes local deployment a two-command operation. The ecosystem is no longer experimental.
Cost and control. At scale, open model inference is 10-100x cheaper than API calls. You own the model weights. You control the data that touches them. You can audit every request. For regulated industries and privacy-sensitive applications, this is not a preference - it is a requirement.
Seven Modules, Full Stack
| Module | Topic | What You Learn |
|---|---|---|
| 1 | Model Ecosystem | Llama, Mistral, Qwen, Gemma, Phi - landscape and selection |
| 2 | Running Locally | llama.cpp, Ollama, LM Studio, hardware requirements |
| 3 | LoRA and QLoRA Fine-Tuning | Theory, implementation, hyperparameters, Unsloth |
| 4 | Quantization in Practice | GGUF, GPTQ, AWQ, bitsandbytes - quality tradeoffs |
| 5 | Fine-Tuning Pipelines | Axolotl, DPO, multi-GPU, dataset preparation |
| 6 | Evaluating Open Models | Benchmarks, custom evals, LLM-as-judge |
| 7 | Production Deployment | vLLM, TGI, multi-adapter serving, autoscaling |
What You Will Be Able to Do
After completing this track, you can:
- Select the right open source model for a given task and hardware budget
- Run any model locally for development and testing
- Fine-tune a model on domain-specific data using LoRA or QLoRA in under a day
- Quantize a model to fit your memory budget with minimal quality loss
- Deploy a multi-model serving stack that scales with demand
- Build eval pipelines that give real signal on open vs. closed model quality
Prerequisites
- Python and basic ML understanding
- Familiarity with transformers and LLMs - LLMs Track
- Access to at least one GPU (a consumer GPU with 8GB+ VRAM covers most lessons)
Start with State of Open Source LLMs for the landscape overview, or jump directly to Running Locally if you want to get something running immediately.