This repository contains three distinct research projects focused on language model development: pre-training (Autolab large-scale autoregressive model training), post-training (NanoChat synthetic task fine-tuning), and inference (llama.cpp optimization benchmarking). Each project follows a disciplined experimentation framework with Hugging Face Jobs integration, experiment tracking via TSV ledgers, and isolated variable modification policies. The pre-training project trains custom GPT architectures with value embeddings and MuonAdamW optimizer on climbmix-400b dataset, evaluating via bits-per-byte. Post-training uses a fixed NanoChat architecture for synthetic math/tool-use/multiple-choice tasks with staged SFT+RL training. Inference benchmarks GGUF model throughput via llama-server OpenAI-compatible API. All projects share common patterns for experiment workflow, HF job management, and results tracking while maintaining strict separation between research domains.