# M8 — annotation model (de-risk / e2e). Same from-scratch transformer as the M5
# winner (RoPE / 16k / three-way tied), but the TARGET is the full annotation wire
# string (⟨T⟩/⟨W⟩/⟨S⟩/⟨G⟩), not English — so the decoder is long (targets average
# ~800 tokens, cap 1024). The PUA separators are already special tokens in the 16k
# vocab (M1), so the wire tokenizes natively.
#
# Minimum-spend de-risk (Jake): reuse the FREE 9.4k M7 silver set (6.8k after
# length-filtering), train locally on the 4090, and ask only "can a from-scratch
# model learn to emit VALID format at all?" Quality is expected to be rough on this
# little data; expand the silver set later if the approach holds.
run_name: m8-annotate
seed: 1

data:
  train: data/processed/m8_annot/train.jsonl
  valid: data/processed/m8_annot/valid.jsonl
  bin_dir: data/processed/m8_annot/tok
  max_src_len: 128
  max_tgt_len: 1024          # annotation wire is long (p95 ~1620; we filtered to <=1024)

tokenizer:
  path: data/processed/tokenizer-16k.json
  vocab_size: 16000

model:
  d_model: 512
  n_heads: 8
  d_ff: 2048
  encoder_layers: 6
  decoder_layers: 6
  dropout: 0.1
  pos_encoding: rope         # relative — handles the long decoder cleanly (M5 winner)
  tie_embeddings: three_way
  norm: pre

train:
  tokens_per_batch: 16000    # target-token budget; ~long targets => fewer seqs/batch
  grad_accum_steps: 2
  max_steps: 8000            # ~small data, many passes; watch valid loss for overfit
  lr: 4.0e-4
  warmup_steps: 500
  label_smoothing: 0.1
  precision: bf16
  compile: true
  checkpoint_every: 999999
  eval_every: 500
  log_every: 100

wandb:
  project: kanjiland
  tags: [m8, annotate]