Synthyra/DPLM-150M

This checkpoint packages the FastPLMs DPLM implementation.

Accepted inputs are amino-acid sequences tokenized to masked or partially masked residue IDs. Supported Transformers entry points are AutoConfig, AutoModel, AutoModelForMaskedLM, AutoModelForSequenceClassification, AutoModelForTokenClassification.

Capabilities

Feature	Status
Sequence classification	Supported: base weights with an untrained task head
Token classification	Supported: base weights with an untrained task head
PEFT fine-tuning	Supported pattern: preserve the separately trained classifier
Embeddings	Supported: shared ordered embedding API
Test-time training	Supported: low-rank masked-residue adaptation
Attention variants	Supported: eager, sdpa, flex_attention, flash_attention_3
Compliance	Declared: exact release evidence is required

A supported interface is not a pretrained downstream predictor. Classification heads start untrained, and declared compliance metadata is not a claim that an arbitrary local build passed its release gate.

Install and platform requirements

Install the direct dependencies published with this model:

python -m pip install -r \
  "https://huggingface.co/Synthyra/DPLM-150M/resolve/main/requirements.txt"

The FastPLMs implementation itself is embedded in the model repository and loaded by Transformers through trust_remote_code=True.

Python 3.11-3.14, PyTorch 2.13, and Transformers 5.13 are required. The artifact requirements include the direct FlashAttention loader dependency. FlashAttention also requires compatible CUDA hardware and BF16 execution. The Hub quick start below requires network access on first download. For an air-gapped run, first build the manifest-pinned local artifact and use the offline form shown in the example.

Quick start

from transformers import AutoModel

model_id = "Synthyra/DPLM-150M"
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/DPLM-150M path and pass local_files_only=True.

Attention and compliance

The quick start selects sdpa explicitly. Declared variants are eager, sdpa, flex_attention, flash_attention_3. An unavailable requested backend raises instead of silently switching implementations. output_attentions=True may use the documented, one-call eager fallback solely to materialize attention tensors; the configured backend remains unchanged.

This family declares the compliance tier. Release evidence binds the exact checkpoint, backend, dtype, hardware, inputs, and reference revision.

Tokenization and forward inference

Load the tokenizer from the same artifact as the model. Padding is represented explicitly by the attention mask:

import torch
from transformers import AutoTokenizer

model_id = "Synthyra/DPLM-150M"
tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True,
)
batch = tokenizer(
    ["MSTNPKPQRKTKRNT", "MKTIIALSYIFCLVFA"],
    padding=True,
    return_tensors="pt",
)

with torch.inference_mode():
    output = model(**batch)

print(output.last_hidden_state.shape)

Dataset embeddings

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

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 persistence. Resume verifies input order, model state, tokenizer policy, backend, dtype, and pooling configuration before appending.

Downstream classification

Both downstream AutoClasses reuse the checkpoint backbone and initialize a new, untrained classifier. Sequence labels have shape (b,); residue labels have shape (b, l) and use -100 outside biological positions:

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

model_id = "Synthyra/DPLM-150M"
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 direct training dependencies, then attach LoRA to the loaded checkpoint:

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

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, so the separately trained classifier is saved with the adapter. All FastPLMs checkpoints follow the Transformers PreTrainedModel contract and can be adapted with 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 remain frozen:

from transformers import AutoModelForMaskedLM

ttt_model = AutoModelForMaskedLM.from_pretrained(
    "Synthyra/DPLM-150M",
    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)

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

Diffusion sequence generation

DPLM defines the requested length from biological positions in a tokenized input, masks those positions, and iteratively retains confident predictions:

import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer

model_id = "Synthyra/DPLM-150M"
tokenizer = AutoTokenizer.from_pretrained(model_id)
generator = AutoModelForMaskedLM.from_pretrained(
    model_id,
    trust_remote_code=True,
).cuda().eval()
input_ids = tokenizer("A" * 64, return_tensors="pt")["input_ids"].cuda()

with torch.inference_mode():
    generated_ids = generator.generate(input_ids, max_iter=100)

sequence = tokenizer.decode(
    generated_ids[0],
    skip_special_tokens=True,
).replace(" ", "")
print(sequence)

Omitting max_iter uses the official 500-step schedule. A shorter schedule changes the sampling process rather than providing an equivalent faster mode.

Plain AutoModel omits the optional ESM pooler because this diffusion checkpoint contains no trained pooler weights. Pass add_pooling_layer=True only when intentionally initializing and training that head.

DPLM1 and DPLM2 checkpoint weights are Apache-2.0. The maintained ByteDance LICENSE is Apache-2.0 and the README explicitly scopes the repository release to the pretrained DPLM1 and DPLM2 weights. FastPLMs artifacts record weights_license_status="resolved" and redistributable=true; complete publication is permitted only after all artifact, legal, parity, and atomic-publication preflights pass.

Runtime contract

• Public input: Amino-acid sequences tokenized to masked or partially masked residue IDs
• Advertised AutoClasses: AutoConfig, AutoModel, AutoModelForMaskedLM, AutoModelForSequenceClassification, AutoModelForTokenClassification
• AutoClass weight status: AutoConfig = FastPLMs extension, AutoModel = pretrained, AutoModelForMaskedLM = pretrained, AutoModelForSequenceClassification = base weights + untrained task head, AutoModelForTokenClassification = base weights + untrained task head
• Attention implementations: eager, sdpa, flex_attention, flash_attention_3
• Precision policies: default
• BF16 execution: fp32_parameters_autocast
• Generation contract: required
• Artifact dependency set: core
• Weight publication allowed: true
• Weight license status: resolved
• Redistributable: true
• Complete weight publication required: false

Release record

• FastPLMs weights: Synthyra/DPLM-150M
• Runtime revision: recorded separately in the built artifact and published commit
• Source-tree and runtime-bundle SHA-256: recorded in provenance.json
• Official checkpoint: airkingbd/dplm_150m
• Artifact source: fast
• State transform: dplm_to_fastplms_v1
• Pinned upstreams: dplm
• Release tiers: check, compliance, feature, artifact, benchmark
• Unresolved required file identities: 0

provenance.json records exact file identities, conversion, source revisions, legal texts, schema, and attestations. A nonzero unresolved count blocks release.

Validation boundary

Declared tiers compare applicable configuration, tokenizer behavior, state, and representative inference with the pinned reference. Metadata alone does not claim a build passed, a backend is faster, or an output is biologically valid.

License

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