People searching for “how to train your own LLM” usually want a usable specialist model, not a multi-million-dollar pre-training run. In practice that means starting from an open base model, teaching it a narrow task with post-training (SFT, LoRA/QLoRA, sometimes DPO), measuring the result on frozen evals, and deciding whether to ship, retry, or reject.

Pre-training vs training your own specialist

Pre-training builds a base model from huge corpora on large GPU clusters. It is the wrong default for almost every individual developer and most small teams.

Post-training / fine-tuning adapts an existing base model to a defined behavior: file operations, SQL hygiene, a tool-use style, or another bounded skill. This is the path PostTrainLLM optimizes for on one Apple Silicon machine.

If a guide promises “train ChatGPT from scratch on a laptop,” treat that claim as marketing, not engineering.

A practical loop

Write the target. Task, inputs, outputs, and what the model must refuse.
Freeze evals first. Hold out tests before you touch training data.
Pick a base model that fits memory. On Apple Silicon, MLX LoRA/QLoRA is the usual starting path.
Train adapters, not identity myths. Prefer LoRA/QLoRA over full-weight training until you have a measured need.
Compare against the unchanged baseline. Report gains and regressions, not only train loss.
Package with evidence. Export the adapter/runtime notes and keep the report card.

Where PostTrainLLM fits

PostTrainLLM is a Mac-local specialist factory: distill or fine-tune, gate on real evals (including BFCL, τ-bench, lm-eval, and SQL checks), package for MLX, and inspect failures. Public specialist artifacts show both wins and regressions—for example a file-operations specialist that lifts a hard gate from 58% to 100% while narrowing out-of-domain breadth. That tradeoff is intentional and should be routed, not hidden.

Related guides

CTA: Start with the Mac fine-tuning guide, then open the public specialist artifacts to see what measured post-training looks like.