Synthyra / DPLM-650M

NOTE

The GitHub with the implementation and requirements can be found here .

DPLM

Synthyra DPLM checkpoints are HuggingFace AutoModel compatible and include FastPLMs embedding helpers.

Supported models

model_dict = {
    "Synthyra/DPLM-150M": "airkingbd/dplm_150m",
    "Synthyra/DPLM-650M": "airkingbd/dplm_650m",
    "Synthyra/DPLM-3B": "airkingbd/dplm_3b",
}

Use with transformers

import torch
from transformers import AutoModel, AutoModelForMaskedLM

model_path = "Synthyra/DPLM-150M"
model = AutoModel.from_pretrained(model_path, trust_remote_code=True, dtype=torch.float16).eval()
tokenizer = model.tokenizer

batch = tokenizer(["MPRTEIN", "MSEQWENCE"], padding=True, return_tensors="pt")
with torch.no_grad():
    hidden = model(**batch).last_hidden_state

mlm = AutoModelForMaskedLM.from_pretrained(model_path, trust_remote_code=True, dtype=torch.float16).eval()
with torch.no_grad():
    logits = mlm(**batch).logits

Attention backend

sdpa is the default backend. Flex Attention is available by setting config.attn_backend = "flex" before loading.

Embed datasets

All DPLM models inherit EmbeddingMixin , so you can call model.embed_dataset(...) directly.