README.md

metadata

library_name: transformers
tags: []

NOTE

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

DPLM2

Synthyra DPLM2 checkpoints are HuggingFace AutoModel compatible and include FastPLMs embedding helpers.

Supported models

model_dict = {
    "Synthyra/DPLM2-150M": "airkingbd/dplm2_150m",
    "Synthyra/DPLM2-650M": "airkingbd/dplm2_650m",
    "Synthyra/DPLM2-3B": "airkingbd/dplm2_3b",
}

Use with transformers

import torch
from transformers import AutoModel, AutoModelForMaskedLM

model_path = "Synthyra/DPLM2-150M"
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

DPLM2 modality types

DPLM2 infers type_ids automatically from input_ids and attention_mask when they are not provided.

Attention backend

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

Embed datasets

All DPLM2 models inherit EmbeddingMixin, so you can call model.embed_dataset(...) directly.