UltraMS

Encode an MS/MS spectrum in three lines. Fine-tune the same encoder with your own labels.

pip install "ultrams[hub]"

from ultrams import UltraMS

model = UltraMS.from_hub("dsadd4/UltraMS-RT-only")
embedding = model.encode(
    mz=[100.1, 121.1, 150.0], intensity=[20, 100, 35], precursor_mz=301.2
).embedding  # NumPy vector

Fine-tune

encode_tensor keeps gradients. Add a task head and train the encoder and head together:

import torch
from ultrams import UltraMS

model = UltraMS.from_hub("dsadd4/UltraMS-RT-only").train()
head = torch.nn.Linear(model.embedding_dim, 1)
optimizer = torch.optim.AdamW([*model.parameters(), *head.parameters()], lr=1e-5)

for mz, intensity, precursor_mz, target in training_rows:
    prediction = head(model.encode_tensor(mz, intensity, precursor_mz=precursor_mz))
    loss = torch.nn.functional.mse_loss(prediction.flatten(), torch.tensor([target], dtype=torch.float32))
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

training_rows contains your labelled spectra. For MoNA or UltraAtlas fine-tuning, replace the model ID in the same code.

Checkpoints

All three can also be loaded from a downloaded model.pt with UltraMS.from_checkpoint(path).

The current pure Ae3 pretraining and RT/ion-mode adaptation source is in training/.