Synthyra / ESMFold2-600

ESMFold2-600

Quick start

Load the published model, fold two protein chains together, and write an mmCIF file. This example uses 15 diffusion steps, matching the experimental config.

import torch

from pathlib import Path
from transformers import AutoModel


model = AutoModel.from_pretrained(
    "Synthyra/ESMFold2-600",
    trust_remote_code=True,
    dtype=torch.float32,
    device_map="cuda",
    esmc_precision="bf16",
    attn_implementation="sdpa",
).eval()
model.set_chunk_size(32)

types = model.input_types
complex_input = types.StructurePredictionInput(
    sequences=[
        types.ProteinInput(id="A", sequence="MSTNPKPQRKTKRNT"),
        types.ProteinInput(id="B", sequence="MKTIIALSYIFCLVFA"),
    ]
)
with torch.inference_mode():
    result = model.fold(
        complex_input,
        num_loops=3,
        num_sampling_steps=15,
        num_diffusion_samples=1,
        seed=17,
        verbose=True,
    )
Path("complex.cif").write_text(model.result_to_cif(result), encoding="utf-8")

Set verbose=False to silence the folding progress display. This variant has a Synthyra-adapted native confidence head and returns pLDDT, PAE, pTM, and iPTM fields. Confidence calculation is optional.

Confidence training and evaluation

The packaged model includes the trained confidence head and enables it by default.

The confidence head completed 780 training updates in 18.1 hours on AtlasFold-Data . The backbone and folding model stayed frozen, and evaluation used the final exponential moving-average checkpoint. Training sampled from 475,969 eligible structures, including monomers, dimers, and larger complexes.

The results below use 512 targets from the existing, already-used test split, with 5 predictions per target, 3 recycling loops, and 50 diffusion steps. Intervals are 95% bootstrap intervals over targets. Longer sequences were evaluated separately and are not included in these tables.

Confidence scores, errors, accuracies, and fractions use a 0–1 scale. Lower error, cross-entropy, and regret are better; higher correlation, ranking accuracy, and AUROC are better. Ranking accuracy compares predictions of the same target, with 0.5 representing chance. Regret is the quality lost by selecting a prediction instead of the best available one. Unresolved residues are a proxy for disorder, not definitive disorder labels.

Production agreement compares each model's average confidence per target: 512 targets for pLDDT and pTM, and 320 multichain targets for ipTM. Differences are ESMFold2-600 minus production. Each model predicts its own structures, so these correlations do not measure folding accuracy or scores of identical structures.

See the FastPLMs confidence training guide for the training recipe, evaluation methods, and detailed records.

Model overview

Synthyra/ESMFold2-600 packages the biohub/ESMFold2-Experimental-Fast-base600M-step1500k checkpoint with the FastPLMs runtime and a Synthyra-adapted native confidence head for Hugging Face Transformers. It accepts raw amino-acid sequences or typed molecular-complex specifications; low-level forward accepts prepared feature tensors.

The repository uses the standard Transformers loading interface with trust_remote_code=True . See Technical details for each registered class and whether its weights come from the checkpoint.

The sequence- and token-classification classes reuse the pretrained backbone, but their task heads are newly initialized. Fine-tune those heads before interpreting their logits as predictions.

Install and platform requirements

Install the direct dependencies published with this model:

python -m pip install -r \
  "https://huggingface.co/Synthyra/ESMFold2-600/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.

Validation runs in Docker on any compatible CUDA device. Record the container, hardware, precision, and inputs; no GPU product or workstation is required.

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.

Attention backends

The quick start uses sdpa .

Available backends are eager , sdpa , flex_attention . Requesting an unavailable backend raises instead of silently changing implementation.

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

Downstream prediction

The sequence and token prediction AutoClasses use the checkpoint backbone and create a new, untrained classifier . Sequence labels have shape (b,) . Residue labels have shape (b, l) and use -100 outside biological positions. The folding trunk is skipped. The classifier uses the checkpoint's learned pLM state mixture and projection, followed by one trainable transformer probe.

import torch

from transformers import (
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
)


model_id = "Synthyra/ESMFold2-600"
sequence_model = AutoModelForSequenceClassification.from_pretrained(
    model_id, num_labels=2, trust_remote_code=True
).eval()
token_model = AutoModelForTokenClassification.from_pretrained(
    model_id, num_labels=3, trust_remote_code=True
).eval()
sequences = ["MSTNPKPQRKTKRNT", "MKTIIALSYIFCLVFA"]
batch = sequence_model.prepare_classifier_inputs(sequences)
biological = batch["attention_mask"].bool()  # (b, l)

sequence_labels = torch.zeros(len(sequences), dtype=torch.long)  # (b,)
token_labels = torch.full_like(batch["input_ids"], -100)  # (b, l)
token_labels[biological] = 0  # selected biological positions; labels stay (b, l)

with torch.inference_mode():
    sequence_output = sequence_model(**batch, labels=sequence_labels)
    token_output = token_model(**batch, labels=token_labels)
print(sequence_output.logits.shape)  # (b, 2)
print(token_output.logits.shape)     # (b, l, 3)

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, TaskType, get_peft_model


peft_model = get_peft_model(
    sequence_model,
    LoraConfig(
        task_type=TaskType.SEQ_CLS,
        r=8,
        lora_alpha=16,
        target_modules="all-linear",
        modules_to_save=["classifier"],
    ),
)

This checkpoint advertises a classification head. Save the separately trained classifier with the adapter. 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 folding

This experimental Fast checkpoint has 24 folding blocks and uses the frozen Synthyra/ESMplusplus_large backbone. The config-declared step-1500000 backbone and the pinned ESM++ weights are tensor-exact in BF16 after layout conversion.

import torch


model = model.cuda().eval()
with torch.inference_mode():
    output = model.infer_protein(
        "MQYKLILNGKTLKGETTTEAVDAATAEKVFKQYANDNGVDGEWTYDDATKTFTVTE",
        seed=17,
        num_diffusion_samples=1,
    )
print(output.sample_atom_coords.shape)

Folding parameters remain FP32 with CUDA BF16 autocast. The backbone uses BF16; FP8 requests fail. The 15-step sampler and three folding loops remain the checkpoint defaults. Protein inputs require msa=None . This checkpoint was trained without MSA conditioning. It rejects ProteinInput.msa and MSA-derived features. Typed multichain and multimolecule inputs remain supported without MSA conditioning.

The Synthyra-adapted native confidence head returns pLDDT, PAE, pTM, and iPTM. Confidence calculation is optional. The 300 and 600 suffixes describe backbone scale, not total model parameters.

Folding speed settings

Two runtime settings trade memory or exactness for speed on long proteins. They need no extra package and no compilation, and neither is stored in the configuration.

model.set_chunk_size(None)            # unchunked pair updates
model.set_atom_attention("windowed")  # the official flash-attn atom window, through PyTorch

set_chunk_size(None) removes the row chunking of the pair-update blocks, which costs most of a long fold's time on a data-center GPU and saves little peak memory; pass a chunk such as 512 when the unchunked fold does not fit. set_atom_attention("windowed") restricts each atom to 64 real neighbors on each side, as the official model does when flash-attn is installed. It needs CUDA and changes numerical output, within sampling spread on the measured panel. The ESMFold2 guide records the conditions, the dense-versus-windowed comparison, and the figure.

Measured on one NVIDIA H100 80GB HBM3 with PyTorch 2.13.0+cu130: one fixed pseudo-random protein per length, 3 trunk loops, 50 requested sampling steps under the official noise cap, 1 diffusion sample, BF16 autocast over FP32 folding parameters, median of end-to-end folds. "Defaults" changes no setting.

Learned representation and ESMC precision

The learned projection maps H: (b, l, 37, 1152) -> Z: (b, l, 256) . embed_dataset returns one (l, 256) residue representation per sequence. The experimental architecture does not expose folding TTT.

Notes and limitations

Experimental Fast model with a frozen 600M ESM++ backbone, 24 folding blocks, no MSA conditioning, and a Synthyra-trained confidence head enabled by default. BF16 execution uses FP32 folding parameters with CUDA autocast; FP8 is unsupported. Confidence evaluation does not establish full structure-model equivalence to production ESMFold2.

Technical details

• Inputs: Raw amino-acid sequences or typed molecular-complex specifications; low-level forward accepts prepared feature tensors
• Transformers classes: AutoConfig , AutoModel , AutoModelForSequenceClassification , AutoModelForTokenClassification
• Checkpoint weights: AutoConfig = FastPLMs extension , AutoModel = pretrained , AutoModelForSequenceClassification = base weights + untrained task head , AutoModelForTokenClassification = base weights + untrained task head
• Attention backends: eager , sdpa , flex_attention
• Precision: auto , fp32 , bf16
• BF16 execution: fp32_parameters_autocast
• Generation contract: not_applicable
• Dependencies: core + structure
• Weight publication allowed: true
• Weight license status: resolved
• Redistributable: true
• Complete weight publication required: false

Validation and sources

FastPLMs pins the checkpoint, upstream source revisions, state transformation, and required files in models.toml . Built artifacts record exact source identities and conversion details in source-record.json .

• FastPLMs checkpoint: Synthyra/ESMFold2-600
• Runtime revision: recorded separately in the built artifact and published commit
• Runtime source identities: recorded in source-record.json
• Official checkpoint: biohub/ESMFold2-Experimental-Fast-base600M-step1500k
• Artifact source: fast
• State transform: identity
• Pinned upstreams: biohub-esm , biohub-transformers , protein-ttt
• Release tiers: check , compliance , structure , feature , artifact , benchmark
• Unresolved required file identities: 0

The confidence evaluation above uses the existing test split. It does not establish full structure-model equivalence.

Declared tiers compare configuration, tokenizer behavior, state, and representative inference with the pinned reference. A nonzero unresolved count blocks release. Metadata alone 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.