Usage: reference in-repo train_gpt_ref.py and glint_parity_eval.py

README.md

@@ -42,16 +42,17 @@ varying only tokens and model width.
 ## Usage
 
 These are raw nanoGPT-lineage checkpoints (plain `torch` state dicts), **not** `transformers` `AutoModel`
-weights. Architecture is inferable from tensor shapes and the table above.
+weights. The model class and a ready board-scoring harness are included in this repo:
+
+- `train_gpt_ref.py` — GPT definition (rebuild the GPT of the tabled shape, `load_state_dict`, trim logits to vocab 12288).
+- `glint_parity_eval.py` — the exact Glint board-scoring forward (256-token clip, raw log-prob) for BLiMP / ARC-Easy / WikiText-2.
 
 ```python
 import torch
 from tokenizers import Tokenizer
 tok   = Tokenizer.from_file("tokenizer.json")          # BPE-12k, vocab 12288
 ckpt  = torch.load("bpe16m_10B/ckpt.pt", map_location="cpu")
-state = ckpt.get("model", ckpt)                        # nanoGPT-style GPT state dict
-# Rebuild a GPT of the tabled shape (e.g. L6 d408 h6, block 1024, tied embeddings),
-# load_state_dict(state), trim logits to vocab 12288, run token ids [B, T].
+state = ckpt.get("model", ckpt)                        # load into the GPT from train_gpt_ref.py
 ```
 
 ## Training data