Synthyra/FastESMFold

This checkpoint contains the FastPLMs ESMFold implementation.

Accepted inputs are raw amino-acid sequences through folding helpers, or prepared residue tensors. 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	Unavailable for this structure-only checkpoint
Test-time training	Unavailable: the checkpoint has no trained MLM head
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/FastESMFold/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 artifact requirements include the structure dependencies. The release contract requires a CUDA device. The current validated target is the exact NVIDIA GH200 on Linux aarch64. Linux x86-64, CPU-only, Windows, and macOS structure runs are not release evidence. 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/FastESMFold"
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/FastESMFold 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.

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.

Protein structure prediction

ESMFold accepts a raw sequence and returns structure tensors and confidence:

import torch

model = model.cuda().eval()
with torch.inference_mode():
    output = model.infer(
        "MKTLLILAVVAAALA",
        num_recycles=4,
    )

print(output["mean_plddt"])

summary = model.fold_protein(
    "MKTLLILAVVAAALA",
    return_pdb_string=True,
)
with open("prediction.pdb", "w", encoding="utf-8") as handle:
    handle.write(summary["pdb_string"])
print(summary["plddt"], summary["ptm"])

FastPLMs does not expose ProteinTTT for ESMFold. The pinned folding checkpoint has no trained masked-language-model head for this objective. ttt() and TTT folding requests raise.

Runtime contract

• Public input: Raw amino-acid sequences through folding helpers, or prepared residue tensors
• 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 + structure
• Weight publication allowed: true
• Weight license status: resolved
• Redistributable: true
• Complete weight publication required: false

Release record

• FastPLMs weights: Synthyra/FastESMFold
• Runtime revision: recorded in the built artifact and published commit
• Source-tree and runtime-bundle SHA-256: recorded in the source record
• Official checkpoint: facebook/esmfold_v1
• Artifact source: fast
• State transform: esmfold_meta_to_fastplms_v1
• Pinned upstreams: fair-esm, openfold
• Release tiers: check, compliance, structure, 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.