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.

from pathlib import Path

import torch
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. The confidence fields are unavailable because this experimental variant has a disabled confidence head.

Model overview

Synthyra/ESMFold2-600 packages the biohub/ESMFold2-Experimental-Fast-base600M-step1500k checkpoint with the FastPLMs runtime 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()

sequence_labels = torch.zeros(len(sequences), dtype=torch.long)
token_labels = torch.full_like(batch["input_ids"], -100)
token_labels[biological] = 0

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 confidence head is disabled: pLDDT, pTM, iPTM, and PAE are unavailable. The 300 and 600 suffixes describe backbone scale, not total model parameters.

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 checkpoint with a frozen 600M ESM++ backbone, tensor-exact in BF16 with the pinned step-1500000 source, 24 folding blocks, no MSA conditioning, and no confidence head. BF16 execution uses FP32 folding parameters with CUDA autocast. FP8 is unsupported. Configuration, weight identities, and artifact reload are verified; this model is not inference-validated.

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

ESMFold2-300 passed a Docker BF16 reference comparison on one compact Protein G sequence. ESMFold2-600 is not inference-validated. Both have configuration, weight identity, and artifact loading checks. This is bounded checkpoint evidence, not a full structure benchmark result.

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.