Phase 1: from-scratch byte-level BPE tokenizer

- src/tokenizer.py: byte-level BPE with GPT-2-style word pre-tokenization,
  protected <|endoftext|> special token, save/load as inspectable JSON.
  Simple recount-per-merge trainer over unique words.
- tests/test_tokenizer.py: round-trip, special-token integrity, unicode,
  lossless pre-tokenization, save/load, determinism (8 tests, no pytest dep)
- scripts/train_tokenizer.py: train on a corpus sample, report bytes/token

README.md

@@ -130,6 +130,7 @@ experiment-llm/
 ├── requirements.txt
 ├── configs/            # one file per experiment (v1, v2, ...)
 ├── scripts/            # one-shot entry points: download, encode, plots
+├── artifacts/          # trained tokenizer.json (small, committed)
 ├── src/
 │   ├── tokenizer.py    # BPE: train / encode / decode
 │   ├── dataset.py      # memmapped token files, batch sampling