{
  "model_type": "nano-proofread",
  "architecture": "decoder-only transformer (pre-norm)",
  "vocab_size": 256,
  "tokenizer": "byte (raw UTF-8 bytes, no vocab file)",
  "dim": 128,
  "n_layers": 4,
  "n_heads": 4,
  "n_kv_heads": 2,
  "head_dim": 32,
  "ffn": "swiglu",
  "ffn_mult": 4,
  "norm": "rmsnorm",
  "norm_eps": 1e-05,
  "positional": "rope",
  "rope_theta": 10000.0,
  "max_seq_len": 64,
  "tie_word_embeddings": true,
  "params": 1016960,
  "training": {
    "task": "short phrase with one common writing error -> corrected phrase",
    "scope": "fixed set of context-dependent confusions (their/there/they're, your/you're, its/it's, then/than, to/too, could have/could of) plus doubled words; NOT general grammar correction",
    "data": "100% code-generated: build a grammatically-correct phrase from templates with rich slot fillers, then inject one error (swap a confusion word for a wrong family member, or double a word); ~15% identity; label correct by construction",
    "objective": "SFT, prompt-masked cross-entropy (only the corrected phrase + newline EOS supervised)",
    "steps": 16000,
    "batch_size": 64,
    "seq_len": 64,
    "optimizer": "adamw",
    "lr": 0.003,
    "schedule": "cosine",
    "warmup_steps": 200,
    "seed": 0
  }
}