Synthyra/Profluent-E1-150M

This checkpoint packages the FastPLMs E1 implementation.

Accepted inputs are raw amino-acid sequences prepared by the native E1 adapter. Supported Transformers entry points are AutoConfig, AutoModel, AutoModelForMaskedLM, AutoModelForSequenceClassification, AutoModelForTokenClassification.

Install and platform requirements

Install the current FastPLMs package:

python -m pip install \
  "fastplms @ git+https://github.com/Synthyra/FastPLMs.git"

Python 3.11-3.14, PyTorch 2.13, and Transformers 5.13 are required. The declared CPU gate covers tiny offline contracts; published checkpoint throughput and parity require the documented device tier. 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/Profluent-E1-150M"
model = AutoModel.from_pretrained(
    model_id,
    trust_remote_code=True,
).eval()

This example uses the published Hub repository. For offline validation, build the manifest-pinned artifact and replace model_id with its local dist/hub/Profluent-E1-150M path, then pass local_files_only=True.

Leave attention unspecified for the Transformers default. Supported explicit choices are sdpa, flex_attention. Pass the selected name through attn_implementation. When an optimized backend cannot return full attention tensors, output_attentions=True emits one explicit runtime warning and uses a correctly masked eager implementation for that call only. The warning identifies the configured backend, effective backend, and reason. Configuration and later calls are unchanged. For BF16 execution, this family uses parameters loaded directly in BF16.

Dataset embeddings

The shared embedding API accepts sequences, (id, sequence) pairs, EmbeddingInput records, insertion-ordered {id: sequence} mappings, or a FASTA path. Results preserve order and duplicate identifiers:

result = model.embed_dataset(
    ["MSTNPKPQRKTKRNT", "MKTIIALSYIFCLVFA"],
    batch_size=2,
    pooling=("mean", "std"),
)

for record in result:
    print(record.id, record.sequence, record.tensor.shape)

Set full_embeddings=True for one residue tensor with shape (l, d) per sequence. Set output to a directory for bounded-memory, transactional safetensors with ordered-prefix resume, or choose format="sqlite" for batch-level database commits and exact resume. Pooling excludes boundary, padding, and other non-biological positions.

For a long FASTA run, stream completed batches into SQLite:

persisted = model.embed_dataset(
    "proteins.fasta",
    batch_size=64,
    pooling=("mean",),
    output="protein-embeddings.sqlite",
    format="sqlite",
    resume=True,
)

Resume verifies the input order, model state, tokenizer policy, backend, dtype, and pooling configuration. It never appends incompatible records to an existing run.

Tokenizer-free E1 input

E1 has no tokenizer. The model retains native raw-sequence preparation, boundary tokens, sequence positions, and retrieval-augmented context behavior. The ordinary representation path accepts sequences directly:

result = model.embed_dataset(
    ["MSTNPKPQRKTKRNT", "MKTIIALSYIFCLVFA"],
    batch_size=2,
    pooling=("mean",),
)
print(result[0].tensor.shape)

Lower-level masked-language-model calls must use the E1 batch preparer rather than an AutoTokenizer. E1 launch messages and distributed legal files retain the attribution required by the upstream agreement.

Runtime contract

• Public input: Raw amino-acid sequences prepared by the native E1 adapter
• 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: sdpa, flex_attention
• Precision policies: default
• BF16 execution: static_parameters
• Generation contract: not_applicable
• Optional dependency group: core
• Weight publication allowed: true
• Weight license status: resolved
• Redistributable: true
• Complete weight publication required: false

Release record

• FastPLMs weights: Synthyra/Profluent-E1-150M
• Runtime revision: recorded separately in the built artifact and published commit
• Source-tree and runtime-bundle SHA-256: recorded in provenance.json
• Generator/schema version and complete/runtime-only attestations: recorded in provenance.json
• Official checkpoint: Profluent-Bio/E1-150m
• Artifact source: fast
• State transform: e1_to_fastplms_v1
• BF16 execution: static_parameters
• Pinned upstreams: e1
• Reference container: reference-e1
• Release tiers: check, compliance, feature, artifact, benchmark
• Unresolved required file identities: 0

The local artifact records exact file identities, conversion details, source revisions, and legal texts in provenance.json. A nonzero unresolved count is a release blocker.

Validation boundary

For tiers declared by the manifest, the release contract compares applicable semantic configuration, tokenizer behavior, state keys, shapes, dtypes, values, aliases, and representative inference with the pinned official implementation. This metadata does not by itself claim that a particular build passed, that one backend is faster, or that an output has biological or therapeutic validity.

License

Checkpoint terms: Profluent-E1 Clickthrough License Agreement. The Hub model-card identifier is other. Applicable source licenses, notices, attribution, and conversion records are distributed with the local artifact. Review them before use.