Building a Complete LLM Pipeline
Everything from Lessons 01 to 12 is one stage of one pipeline. This lesson is the scaffold that turns those stages into a single end-to-end run: tokenize, pre-train, scale, SFT, align, evaluate, quantize, serve. You will not train a 70B model on a laptop. You will produce the orchestration layer, the manifest, the eval gate, and the rollback plan that a 2026 frontier team uses to decide what gets shipped. This is the capstone. Compose the eleven prior lessons (tokenizer, data, pre-training, scaling, SFT, RLHF, DPO, CAI, eval, quantization, inference) into a single reproducible pipeline spec. Define the artifact contract between stages: what each stage consumes, what it produces, and how the next stage verifies the input. Build an orchestrator that tracks experiments, hashes artifacts, and gates ship decisions on eval thresholds. Design the rollback plan: which artifacts are cheap to re-run, which are expensive, and what a corrupted checkpoint costs. The previous lessons each work. Tokenizer trained. Tiny GPT pre-trained. SFT dataset assembled. Reward model trained. DPO run. Evals measured. Quantized weights exported. Inference server spun up. Each one is a notebook. Each one has its own conventions, its own output paths, its own seed. A frontier training run is not a notebook. Llama 3 405B took 30 million H100 hours over roughly 54 days. DeepSeek-V3 used around 2.8…
Building a Complete LLM Pipeline: Everything from Lessons 01 to 12 is one stage of one pipeline. This lesson is the scaffold that turns those stages into a…
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