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c202bada6a
Phase 2: token dataset + corpus encoder (code only, not yet run)
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- src/dataset.py: memmap uint16 token loader + random-crop batch sampler
(x, y shifted-by-one, reproducible via generator)
- scripts/encode_corpus.py: stream stories -> encode -> uint16 .bin, one EOT
token appended per story, flat memory. encode_file() factored out for testing.
- tests/test_dataset.py: batch shapes/shift/bounds/reproducibility +
encoder round-trip/separators/uint16 on synthetic data (5 tests)
2026-07-12 10:38:20 -04:00
rzen
6111aac459
Mark Phase 1 tokenizer item done
2026-07-12 10:33:33 -04:00
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3a489fef0a
Phase 1: trained 4096-vocab tokenizer + per-word encode cache
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- artifacts/tokenizer.json: 3839 merges, trained on 50MB sample in 87s.
3.97 bytes/token on held-out text.
- encode() caches word->ids so encoding the full corpus is mostly dict
lookups (TinyStories has a small vocabulary).
2026-07-12 10:33:24 -04:00
rzen
2c5fc29592
Phase 1: from-scratch byte-level BPE tokenizer
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- 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
2026-07-12 10:31:16 -04:00
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e760c00043
Mark Phase 0 environment + dataset item done
2026-07-12 10:26:03 -04:00
rzen
0c26cd1f1f
Record canonical TinyStoriesV2 SHA-256 hashes for reproducibility
2026-07-12 10:25:53 -04:00
rzen
5a7f2177bc
Phase 0: project plan, pinned environment, data download + MPS smoke test
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- README with full pipeline plan (tokenizer -> data -> model -> training -> eval)
- pip/venv setup pinned to torch 2.13.0, numpy 2.5.1 (Python 3.13)
- scripts/download_data.py: one-time TinyStoriesV2 fetch with SHA-256 recording
- scripts/check_mps.py: verifies MPS backend, backward pass, GPU speedup
2026-07-12 10:24:52 -04:00
rzen