title: Metalorian / MetaLATTE
Repo structure
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:
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:
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:
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:
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
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
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
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.