Synthyra/ESM3_small

This checkpoint packages the FastPLMs ESM3 implementation.

Accepted inputs are sequence, structure, and function tracks prepared through the multimodal helpers. Supported Transformers entry points are AutoConfig, AutoModel.

Capabilities

Feature	Status
Sequence classification	Unavailable: no advertised AutoClass
Token classification	Unavailable: no advertised AutoClass
PEFT fine-tuning	Supported pattern: attach LoRA to the pretrained model
Embeddings	Supported: shared ordered embedding API
Test-time training	Supported: low-rank masked-residue adaptation
Attention variants	Supported: eager, sdpa, flex_attention
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/ESM3_small/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 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/ESM3_small"
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/ESM3_small path and pass local_files_only=True.

Attention and compliance

The quick start selects sdpa explicitly. Declared variants are eager, sdpa, flex_attention. 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.

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.

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, get_peft_model

peft_model = get_peft_model(
    model,
    LoraConfig(
        r=8,
        lora_alpha=16,
        target_modules="all-linear",
    ),
)

This checkpoint has no advertised classifier. Supply the task-specific objective and preserve any new head through modules_to_save. 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 AutoModel

ttt_model = AutoModel.from_pretrained(
    "Synthyra/ESM3_small",
    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.

Sequence inference and masked-sequence generation

ESM3 owns its sequence preparation. This example exercises the sequence track; the public input contract also supports structure and function tracks through the multimodal helpers:

import torch

batch = model.tokenize_sequences(
    ["MKTAYIAKQ", "GGGG"],
    device=model.device,
)
with torch.inference_mode():
    output = model(**batch)

print(output.last_hidden_state.shape)
print(output.logits.shape)
print(output.structure_logits.shape)
print(output.function_logits.shape)

When return_dict=False, ESM3 follows the standard base-model tuple prefix: last_hidden_state, then requested hidden_states and attentions. Multimodal logits and extensions follow that prefix. Prefer named fields for individual tracks.

Generate masked sequence positions with an explicit seed:

from fastplms.models.esm3.modeling_esm3 import FastESM3GenerationConfig

config = FastESM3GenerationConfig(
    num_steps=8,
    temperature=1.0,
    seed=7,
)
generated = model.generate("MK____A", config)
print(generated)

Underscores mark positions to generate. Model outputs are predictions over tracks, not experimental measurements of structure or function.

Runtime contract

• Public input: Sequence, structure, and function tracks prepared through the multimodal helpers
• Advertised AutoClasses: AutoConfig, AutoModel
• AutoClass weight status: AutoConfig = FastPLMs extension, AutoModel = pretrained
• Attention implementations: eager, sdpa, flex_attention
• Precision policies: default
• BF16 execution: fp32_parameters_autocast
• Generation contract: not_applicable
• Artifact dependency set: core
• Weight publication allowed: true
• Weight license status: resolved
• Redistributable: true
• Complete weight publication required: false

Release record

• FastPLMs weights: Synthyra/ESM3_small
• Runtime revision: recorded separately in the built artifact and published commit
• Source-tree and runtime-bundle SHA-256: recorded in provenance.json
• Official checkpoint: biohub/esm3-sm-open-v1
• Artifact source: fast
• State transform: esm3_to_fastplms_v1
• Pinned upstreams: biohub-esm, biohub-transformers
• 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: MIT. The Hub model-card identifier is mit. Applicable source licenses, notices, attribution, and conversion records are distributed with the local artifact. Review them before use.