| # medium v2 | |
| - **preset:** medium | |
| - **training corpus:** data/medium (see corpus_index.md / corpus_stats.txt) | |
| - **trained:** 2026-07-03 16:04 (checkpoint mtime) | |
| - **training wall-clock:** unknown | |
| - **final val loss:** 1.2278 (iter 7999) | |
| - **parameters:** 25.44M | vocab 97 | |
| ## Training hyperparameters | |
| | param | value | | |
| |---|---| | |
| | block_size | 256 | | |
| | n_embd | 512 | | |
| | n_head | 8 | | |
| | n_layer | 8 | | |
| | dropout | 0.0 | | |
| | batch_size | 40 | | |
| | max_iters | 8000 | | |
| | eval_interval | 500 | | |
| | eval_iters | 80 | | |
| | learning_rate | 0.0003 | | |
| | vocab_size | 97 | | |
| ## Validation curve | |
| | step | train | val | | |
| |---:|---:|---:| | |
| ## Reproducing the corpus | |
| `corpus_index.md` lists every author and work in the training set; re-run `python -m corpus add-author` / `add-topic` per that index and `make finalize` to rebuild an equivalent corpus. | |