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Synthyra/
DPLM2-650M

Name: DPLM2-650M
Availability: InStock

transformers

safetensors

esm

protein-language-model

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DPLM2-650M

Model overview

Synthyra/DPLM2-650M packages the airkingbd/dplm2_650m checkpoint with the FastPLMs runtime for Hugging Face Transformers. It accepts tokenized amino-acid and structure tracks with explicit modality boundaries.

The repository uses the standard Transformers loading interface with trust_remote_code=True. See Technical details for each registered class and whether its weights come from the checkpoint.

The sequence- and token-classification classes reuse the pretrained backbone, but their task heads are newly initialized. Fine-tune those heads before interpreting their logits as predictions.

Install and platform requirements

Install the direct dependencies published with this model:

bash

python -m pip install -r \
  "https://huggingface.co/Synthyra/DPLM2-650M/resolve/main/requirements.txt"

The FastPLMs implementation itself is embedded in the model repository. Transformers loads it through trust_remote_code=True.

This model requires Python 3.11-3.14, PyTorch 2.13, and Transformers 5.13.

The CPU gate covers small offline tests. Published checkpoint throughput and parity require the documented device tier.

The Hub quick start needs network access for the first download. For an air-gapped run, build the manifest-pinned local artifact first and use the offline example.

Quick start

python

from transformers import AutoModel

model_id = "Synthyra/DPLM2-650M"
model = AutoModel.from_pretrained(
    model_id,
    trust_remote_code=True,
    attn_implementation="sdpa",
).eval()

For offline validation, replace model_id with the manifest-built dist/hub/DPLM2-650M path. Pass local_files_only=True.

Attention backends

The quick start uses sdpa.

Available backends are sdpa. Requesting an unavailable backend raises instead of silently changing implementation.

output_attentions=True can use the documented one-call eager fallback to materialize attention tensors. The configured backend does not change.

Dataset embeddings

The shared embedding mixin keeps input order and biological-position masking. It accepts sequences, identified records, mappings, or a FASTA path:

python

pooled = model.embed_dataset(
    ["MSTNPKPQRKTKRNT", "MKTIIALSYIFCLVFA"],
    batch_size=2,
    pooling=("mean", "std"),
)
residues = model.embed_dataset(
    ["MSTNPKPQRKTKRNT"],
    full_embeddings=True,
)
print(pooled[0].tensor.shape)   # (2 * d,)
print(residues[0].tensor.shape) # (l, d)

Set output and format="safetensors" or "sqlite" for transactional, bounded-memory storage. Resume checks input order, model state, tokenizer policy, backend, dtype, and pooling configuration before it appends data.

Downstream prediction

The sequence and token prediction AutoClasses use the checkpoint backbone and create a new, untrained classifier. Sequence labels have shape (b,). Residue labels have shape (b, l) and use -100 outside biological positions.

python

import torch
from transformers import AutoTokenizer
from transformers import (
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
)

model_id = "Synthyra/DPLM2-650M"
sequence_model = AutoModelForSequenceClassification.from_pretrained(
    model_id, num_labels=2, trust_remote_code=True
).eval()
token_model = AutoModelForTokenClassification.from_pretrained(
    model_id, num_labels=3, trust_remote_code=True
).eval()
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
sequences = ["MSTNPKPQRKTKRNT", "MKTIIALSYIFCLVFA"]
batch = tokenizer(sequences, padding=True, return_tensors="pt")
biological = batch["attention_mask"].bool()
for special_id in tokenizer.all_special_ids:
    biological &= batch["input_ids"].ne(special_id)

sequence_labels = torch.zeros(len(sequences), dtype=torch.long)
token_labels = torch.full_like(batch["input_ids"], -100)
token_labels[biological] = 0

with torch.inference_mode():
    sequence_output = sequence_model(**batch, labels=sequence_labels)
    token_output = token_model(**batch, labels=token_labels)
print(sequence_output.logits.shape)  # (b, 2)
print(token_output.logits.shape)     # (b, l, 3)

PEFT fine-tuning

Install the training dependencies. Then attach LoRA to the loaded checkpoint:

python -m pip install "datasets>=4.8,<5" "peft>=0.19,<0.20"

python

from peft import LoraConfig, TaskType, get_peft_model

peft_model = get_peft_model(
    sequence_model,
    LoraConfig(
        task_type=TaskType.SEQ_CLS,
        r=8,
        lora_alpha=16,
        target_modules="all-linear",
        modules_to_save=["classifier"],
    ),
)

This checkpoint advertises a classification head. Save the separately trained classifier with the adapter. All FastPLMs checkpoints follow the Transformers PreTrainedModel contract and can use PEFT. The ESM2-specific shipped CLI is an example, not a support boundary. Record the target modules, base revision, data identity, and trainable parameter scope.

Test-time training

TTT samples masked views of one protein and updates only injected low-rank adapters. Base checkpoint weights stay frozen:

python

from transformers import AutoModelForMaskedLM

ttt_model = AutoModelForMaskedLM.from_pretrained(
    "Synthyra/DPLM2-650M",
    trust_remote_code=True,
)
metrics = ttt_model.ttt(
    seq="MSTNPKPQRKTKRNT",
    ttt_config={"steps": 3, "batch_size": 1, "seed": 7},
)
ttt_model.save_pretrained("adapted", safe_serialization=True)
ttt_model.ttt_reset()
print(metrics)

Saved adapters retain their deterministic reset state. TTT adds latency and memory, can worsen an output, and does not show biological function.

Amino-acid and structure co-generation

DPLM2 uses separate structure and amino-acid tracks. Each track has its own boundary and mask tokens:

python

import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer

model_id = "Synthyra/DPLM2-650M"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
generator = AutoModelForMaskedLM.from_pretrained(
    model_id,
    trust_remote_code=True,
).cuda().eval()
vocab = tokenizer.get_vocab()
l = 64
structure = [
    vocab["<cls_struct>"],
    *([vocab["<mask_struct>"]] * l),
    vocab["<eos_struct>"],
]
amino_acids = [
    vocab["<cls_aa>"],
    *([vocab["<mask_aa>"]] * l),
    vocab["<eos_aa>"],
]
input_ids = torch.tensor([structure + amino_acids], device="cuda")

with torch.inference_mode():
    generated = generator.generate(input_ids, max_iter=100)["output_tokens"]
print(generated.shape)

Generic cls_token, eos_token, mask_token, and unk_token aliases are not set. Code that creates multimodal tensors must select the amino-acid or structure token explicitly. Raw amino-acid sequences remain supported by model.embed_dataset(...).

Plain AutoModel omits the optional ESM pooler because this co-generation checkpoint has no trained pooler weights. Pass add_pooling_layer=True only when you intend to initialize and train that head.

The checkpoint weights use Apache-2.0. The ByteDance LICENSE and README document the license for pretrained DPLM1 and DPLM2 weights. Complete publication requires all artifact, legal, parity, and atomic-publication checks.

Technical details

Inputs: Tokenized amino-acid and structure tracks with explicit modality boundaries
Transformers classes: AutoConfig, AutoModel, AutoModelForMaskedLM, AutoModelForSequenceClassification, AutoModelForTokenClassification
Checkpoint weights: AutoConfig = FastPLMs extension, AutoModel = pretrained, AutoModelForMaskedLM = pretrained, AutoModelForSequenceClassification = base weights + untrained task head, AutoModelForTokenClassification = base weights + untrained task head
Attention backends: sdpa
Precision: default
BF16 execution: fp32_parameters_autocast
Generation contract: required
Dependencies: core
Weight publication allowed: true
Weight license status: resolved
Redistributable: true
Complete weight publication required: false

Validation and provenance

FastPLMs pins the checkpoint, upstream source revisions, state transformation, and required files in models.toml. Built artifacts record exact source identities and conversion details in source-record.json.

FastPLMs checkpoint: Synthyra/DPLM2-650M
Runtime revision: recorded separately in the built artifact and published commit
Runtime source identities: recorded in source-record.json
Canonical transformed state identity: recorded in source-record.json
Conversion equality attestation: recorded in source-record.json
Official checkpoint: airkingbd/dplm2_650m
Artifact source: official
State transform: dplm2_to_fastplms_v1
Tokenizer class: fastplms.models.dplm2.tokenization_dplm2.DPLM2Tokenizer
Pinned upstreams: dplm
Release tiers: check, compliance, feature, artifact, benchmark
Unresolved required file identities: 0

Release validation includes the compliance tier. Its evidence identifies the checkpoint, backend, dtype, hardware, inputs, and reference revision.

Declared tiers compare configuration, tokenizer behavior, state, and representative inference with the pinned reference. A nonzero unresolved count blocks release. Metadata alone does not show that a build passed, that a backend is faster, or that an output is biologically valid.

License

Checkpoint terms: Apache-2.0. The Hub model-card identifier is apache-2.0. The local artifact contains applicable source licenses, notices, attribution, and conversion records. Review them before use.