| --- |
| title: Metalorian / MetaLATTE |
|
|
| --- |
| |
| # Repo structure |
|
|
| ```text |
| train.py Train either stage with --stage |
| sample_direct.py Direct sampling |
| sample_prototype.py Prototype-seeded sampling |
| metalorian/ Models and support functions |
| data/metalatte/ Training and validation split |
| data/metalorian/ Diffusion sequence-label training and test split |
| data/MbPA/ Original MbPA CSV exports |
| checkpoints/ Predictor and diffusion checkpoints |
| assets/ Local ESM-2 backbone and tokenizer |
| requirements.txt |
| ``` |
|
|
| ## Install |
|
|
| Use Python 3.10. Run from this directory: |
|
|
| ```bash |
| python3.10 -m venv .venv |
| source .venv/bin/activate |
| python -m pip install -r requirements.txt |
| ``` |
|
|
| Weights and tokenizer files are included for offline use after installation. |
| Use a CUDA-capable PyTorch installation. For CPU checks, pass `--device cpu`. |
|
|
| ## Data |
|
|
| | Split | Records | |
| | --- | ---: | |
| | `data/metalatte/train` | 50,694 groups | |
| | `data/metalatte/validation` | 12,729 groups | |
| | `data/metalorian/train` | 38,123 sequence-label records | |
| | `data/metalorian/test` | 9,531 sequence-label records | |
|
|
| MetaLATTE groups are stored in 1,852 training pre-batches and 460 validation |
| pre-batches. |
| Metalorian uses the separate sequence-label splits listed above. |
|
|
| `data/MbPA/` contains eight original CSV exports, aggregated from 14 different metal labels. File names, row counts and |
| hashes are listed in `provenance/mbpa_inventory.csv`. |
|
|
| ## Train either stage |
|
|
| MetaLATTE, initialized from pretrained ESM-2: |
|
|
| ```bash |
| python train.py --stage metalatte --epochs 30 --device cuda:0 \ |
| --output runs/metalatte |
| ``` |
|
|
| To start from the supplied predictor, add |
| `--init-checkpoint checkpoints/metalatte_predictor.ckpt`. |
| To resume training and optimizer state, use `--resume PATH`. |
|
|
| Metalorian, initialized from the supplied MetaLATTE predictor: |
|
|
| ```bash |
| python train.py --stage metalorian --epochs 1 --batch-size 2 --device cuda:0 \ |
| --output runs/metalorian |
| ``` |
|
|
| Use `--init-checkpoint PATH` for another MetaLATTE checkpoint, or |
| `--resume checkpoints/metalorian_diffusion.pt` to resume diffusion training. |
| Here, `--epochs` counts additional passes through the training set. |
| Outputs are `metalorian_latest.pt` in the output directory. |
|
|
| Short forward/backward checks for both stages: |
|
|
| ```bash |
| python train.py --stage metalatte --smoke-test --device cpu \ |
| --init-checkpoint checkpoints/metalatte_predictor.ckpt --output runs/check_metalatte |
| python train.py --stage metalorian --smoke-test --batch-size 2 --device cpu \ |
| --output runs/check_metalorian |
| ``` |
|
|
| `--smoke-test` uses at most two records and 64 tokens for one update and writes |
| no model checkpoint. Training uses one device; see `VALIDATION.md` for checks. |
|
|
| ## Direct sampling |
|
|
| ```bash |
| python sample_direct.py --target_idx 3 --seq_len 30 --batch 1 \ |
| --desired 1 --max_rounds 20 --output outputs/Cu_L30.csv |
| ``` |
|
|
| ## Prototype-seeded sampling |
|
|
| ```bash |
| python sample_prototype.py --target_idx 1 --seq_len 30 --batch 1 \ |
| --proto MDPETCPCPSGGSCTCADSCKCEGCKCTSCKKSCCSCCPAECEKCAKDCVCKGGEAAEAEAEKCSCCQ \ |
| --t_start 15 --desired 1 --max_rounds 20 --output outputs/Cd_L30.csv |
| ``` |
|
|
| ```text |
| Metal indices: 0 Ag, 1 Cd, 2 Co, 3 Cu, 4 Fe, 5 Hg, 6 Mn, 7 Mo, |
| 8 Ni, 9 Pb, 10 Pt, 11 V, 12 W, 13 Zn, 14 NA |
| ``` |
|
|
| Both scripts load `checkpoints/metalatte_predictor.ckpt` and |
| `checkpoints/metalorian_diffusion.pt`. Use `--help` for sampling options. |
|
|
| The CSV contains unique accepted sequences; the adjacent JSON records settings |
| and counts. A run may return fewer than `--desired` at `--max_rounds`. |
