Synthyra/ESM3_small

This checkpoint contains 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. Compliance metadata does not show that a 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. 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

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

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

Dataset embeddings

The shared embedding mixin keeps 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 storage. Resume checks input order, model state, tokenizer policy, backend, dtype, and pooling configuration before it appends data.

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"

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 objective and preserve any new head through modules_to_save. 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:

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)

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

Sequence inference and masked-sequence generation

ESM3 prepares its sequence input. This example uses 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 uses the standard base-model tuple prefix: last_hidden_state, then requested hidden_states and attentions. Multimodal logits and extensions follow this prefix. Use 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 track predictions, 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 in the built artifact and published commit
• Source-tree and runtime-bundle SHA-256: recorded in the source record
• 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

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

Validation boundary

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

License

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