Train, Tune, Quantize, Serve, Evaluate, and Document a Complete Language Model System
End-to-end construction of a complete LLM system from scratch: data curation, pretraining (100M parameters), supervised fine-tuning, DPO alignment, INT8 quantization, vLLM serving deployment, comprehensive evaluation, and full technical documentation. This project demonstrates mastery of the entire LLM stack.
What I Built
End-to-end construction of a complete LLM system from scratch: data curation, pretraining (100M parameters), supervised fine-tuning, DPO alignment, INT8 quantization, vLLM serving deployment, comprehensive evaluation, and full technical documentation. This project demonstrates mastery of the entire LLM stack.
Key Concepts
Architecture
Results
Complete 100M parameter model trained from scratch. 15.2 perplexity on validation. 65% on MMLU, 42% on HumanEval. Serves at 120 tokens/sec on single GPU. Full documentation and reproducibility.
Key Learnings
- End-to-end understanding reveals interactions between components
- Data quality is the foundation everything else builds on
- Serving and evaluation are as complex as training
- Documentation is engineering—reproducibility matters
Challenges
- Coordinating multiple complex pipelines
- Debugging failures across the entire stack
- Achieving reproducibility in distributed training
- Balancing time across all components