Synthyra/Boltz2

This checkpoint packages the FastPLMs Boltz2 implementation.

Accepted inputs are raw amino-acid sequences through the convenience API, or prepared model features. 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 for this inference-only checkpoint
Attention variants	Supported: eager
Compliance	Unavailable: this provisional family has no compliance tier

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/Boltz2/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 structure dependencies. The published execution contract requires a CUDA device. The current validated release target is the exact NVIDIA GH200 on Linux aarch64; Linux x86-64, CPU-only, Windows, and macOS structure runs are not current release evidence. 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/Boltz2"
model = AutoModel.from_pretrained(
    model_id,
    trust_remote_code=True,
    attn_implementation="eager",
).eval()

For offline validation, replace model_id with the manifest-built dist/hub/Boltz2 path and pass local_files_only=True.

Attention and compliance

The quick start selects eager explicitly. Declared variants are eager. 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 does not declare the compliance tier. Boltz2 remains provisional and its structure checks must not be broadened into parity claims.

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.

Protein structure prediction

The high-level helper prepares a protein-only input, runs the declared Boltz2 inference core, and returns coordinates and confidence fields:

import torch

model = model.cuda().eval()
output = model.predict_structure(
    amino_acid_sequence="MSTNPKPQRKTKRNTNRRPQDVKFPGG",
    recycling_steps=3,
    num_sampling_steps=50,
    diffusion_samples=1,
    seed=7,
)
model.save_as_cif(output, "prediction.cif")

print(output.sample_atom_coords.shape)
print(output.plddt, output.ptm, output.iptm)

The validation boundary below describes the currently supported inference subset and its provisional status. The helper scopes and restores Python, NumPy, CPU Torch, and CUDA RNG state. Parameters and prepared features remain FP32; supported CUDA inference executes inside BF16 autocast.

Notes and limitations

Boltz2 is provisional in FastPLMs 1.0. Exact configuration, the declared inference-core state, feature preparation, and seeded execution remain tested, but native-environment BF16 end-to-end inference currently exceeds the fixed numerical-equivalence limits. FastPLMs therefore does not claim official inference equivalence for this checkpoint yet. Work on that numerical gap continues independently of the ESM++ and ESMFold2 release gates.

Runtime contract

• Public input: Raw amino-acid sequences through the convenience API, or prepared model features
• Advertised AutoClasses: AutoConfig, AutoModel
• AutoClass weight status: AutoConfig = FastPLMs extension, AutoModel = pretrained
• Attention implementations: eager
• 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/Boltz2
• Runtime revision: recorded separately in the built artifact and published commit
• Source-tree and runtime-bundle SHA-256: recorded in provenance.json
• Official checkpoint: boltz-community/boltz-2
• Artifact source: fast
• State transform: boltz2_inference_core_v1
• Pinned upstreams: boltz
• Release tiers: structure, 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.