Synthyra/ESMplusplus_large

This checkpoint packages the FastPLMs ESMC implementation.

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

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	Special: SDPA fidelity path; alternate backends have explicit bands
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/ESMplusplus_large/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/ESMplusplus_large"
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/ESMplusplus_large 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_2, 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/ESMplusplus_large"
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.

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 AutoModelForMaskedLM

ttt_model = AutoModelForMaskedLM.from_pretrained(
    "Synthyra/ESMplusplus_large",
    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.

ESMC behavior

This artifact exposes the Biohub ESMC sequence encoder and masked-language-model head through Transformers. It is also the language-model family used by ESMFold2. SDPA is the default and the recommended choice for highest numerical fidelity. Flex Attention and FlashAttention 3 are supported, non-experimental backends, but their BF16 arithmetic may be numerically divergent from SDPA. Those deviations produce diagnostic warnings rather than strict parity failures; dispatch integrity, masks, finite outputs, shapes, and catastrophic biological disagreement remain hard gates.

The current GH200/aarch64 release environment validates eager, SDPA, and Flex. Flash requests fail closed because compatible locked kernels are unavailable on this platform.

When sequence_id is supplied, it is authoritative for ESMC attention grouping and padding, and attention_mask is ignored. Values greater than or equal to zero are valid sequence-group IDs; -1 denotes padding. Omit sequence_id to use attention_mask as the padding contract.

Backend	Support	Measurement status
sdpa	Recommended fidelity path	Pending release measurement
eager	Supported	Pending release measurement
flash_attention_2	Supported	Unavailable on current GH200/aarch64 lock
flex_attention	Supported, numerically divergent	Pending release measurement
flash_attention_3	Supported, numerically divergent	Unavailable on current GH200/aarch64 lock

Detailed backend measurements, release guardrails, and the GH200 package compatibility exception are maintained in the attention backend guide and release evidence manifest.

Runtime contract

• Public input: Amino-acid sequences tokenized to residue IDs
• Advertised AutoClasses: AutoConfig, AutoModel, AutoModelForMaskedLM
• AutoClass weight status: AutoConfig = FastPLMs extension, AutoModel = pretrained, AutoModelForMaskedLM = pretrained
• Attention implementations: eager, sdpa, flex_attention, flash_attention_2, flash_attention_3
• Precision policies: default
• BF16 execution: static_parameters
• 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/ESMplusplus_large
• 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/ESMC-600M
• Artifact source: fast
• State transform: esmc_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.