v0.12.0 — Deployment & CLI

Deployment & CLI — the final feature phase before v1.0. A trained model becomes a
usable artifact: save a self-describing bundle (model type + config + weights +
vocabulary + labels), reload it anywhere with no training code, and run it from the
command line (polaris predict), over HTTP (polaris serve → FastAPI
POST /predict), or in a container (Dockerfile). This closes the end-to-end
story: a person can now train a real model on a real dataset and serve it.

Added

• polaris.inference: turn a trained model into a usable artifact. save_bundle /
  load_bundle persist a self-describing bundle (model type + config + weights +
  vocabulary + labels) and reload it as a ready Predictor; Predictor runs the
  whole raw-text path (tokenize → collate → forward → softmax) and returns a
  Prediction (label + per-class probabilities); build_model reconstructs a
  model from its saved type and config.
• polaris.deployment: a thin HTTP serving layer — create_app wraps a
  Predictor (loaded from a bundle) in a FastAPI app exposing GET /health and
  POST /predict. FastAPI/uvicorn are an optional serving extra, imported lazily.
• CLI commands: polaris predict TEXT --model BUNDLE [--probs] (classify raw text)
  and polaris serve --model BUNDLE (serve it over HTTP).
• A Dockerfile to serve a model in a container (model bundle mounted at run time).
• Vocabulary.to_dict / Vocabulary.from_dict: a validated serialization
  round-trip, used by the bundle format.
• examples/pretrain_finetune_imdb.py now saves a bundle at the end of a run.