Datasets:
File size: 3,393 Bytes
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phase: phase3
data:
ndl_metadata_dir: data/raw/ndl/metadata
ndl_metadata_glob: data/raw/ndl/metadata/books_*.zip
ndl_metadata_urls:
- https://dl.ndl.go.jp/static/files/dataset/dataset_202602_t_internet_01.zip
- https://dl.ndl.go.jp/static/files/dataset/dataset_202602_t_internet_02.zip
- https://dl.ndl.go.jp/static/files/dataset/dataset_202602_t_internet_03.zip
- https://dl.ndl.go.jp/static/files/dataset/dataset_202602_t_internet_04.zip
- https://dl.ndl.go.jp/static/files/dataset/dataset_202602_t_internet_05.zip
ndl_selection_manifest: data/processed/ndl/selection.jsonl
ndl_download_manifest: data/processed/phase3/ndl_downloads.jsonl
ndl_raw_dir: data/raw/ndl/books
ndl_clean_dir: data/clean/ndl
ndl_clean_manifest: data/processed/phase3/ndl_clean.jsonl
ndl_clean_reuse_manifest: data/processed/ndl/clean.jsonl
ndl_clean_report: phase3_ndl_cleaning.json
ndl_quality_sample_report: phase3_ndl_quality_sample.jsonl
ndl_quality_exclude_manifest: data/processed/ndl/clean.jsonl
# At the 10k checkpoint, later books averaged only 60.7k clean characters per
# candidate. 50k projects about 3.24B NDL characters and preserves margin for
# the 1.5B-token gate even if the 16k tokenizer lowers tokens/character.
ndl_target_documents: 50000
ndl_download_delay_seconds: 0.2
ndl_download_retries: 3
ndl_max_download_seconds: 600
split_manifest: data/processed/phase3/splits.jsonl
ndl_max_abnormal_ngram_rate: 0.38
ndl_max_latin_rate: 0.005
tokenizer:
vocab_size: 16384
directory: artifacts/phase3/tokenizer
# Train the BPE vocabulary on a deterministic seeded ~200M-character subset of
# the 1.92B-character train split. The tokenizers BPE trainer with an unsplit
# byte-level pre-tokenizer holds a per-character pair index (~110 bytes/char
# peak here), so full-corpus training needs hundreds of GiB; 200M chars keeps
# peak resident memory near ~23 GiB (no swap) while the 8k/16k vocabulary is
# already fully converged. The subset is reproducible from project.seed and is
# recorded in tokenizer metadata (training_sample_*).
max_train_chars: 200000000
tokenized:
directory: data/processed/phase3/tokenized
model:
# 26 layers compensate for the smaller Japanese vocabulary embedding and
# yield about 346M parameters with a 16,384-token vocabulary.
n_layer: 26
n_head: 16
n_embd: 1024
block_size: 1024
dropout: 0.0
training:
device: cuda
dtype: bfloat16
# A full measured optimizer step uses 41.58GB of 47.67GB (12.8% headroom)
# and projects 3.0B processed tokens to 18.95h on the rented L40S.
batch_size: 32
gradient_accumulation_steps: 8
max_iters: 11445
learning_rate: 0.0003
min_lr: 0.00003
warmup_iters: 300
lr_decay_iters: 11445
eval_interval: 250
eval_iters: 50
checkpoint_interval: 250
grad_clip: 1.0
out_dir: artifacts/phase3
budget:
# Measured train tokens fell below the original 1.5B target (PLAN 3.4 below-target
# case): 1,244,686,991 at vocab 8192 and 1,103,005,885 at vocab 16384. Confirm the
# measured corpus as the budget floor (1.1B) so training proceeds; the model-size
# decision is recorded per vocab in reports/phase3_tokenization.json (8192 keeps
# ~337M; 16384 at <1.2B triggers the 250M-class review).
minimum_train_tokens: 1100000000
maximum_hours: 30
maximum_usd: 45
gpu_hourly_usd: 0.99
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