Synthyra / Boltz2

NOTE

The GitHub with the implementation and requirements can be found here .

Boltz2 AutoModel (Inference-only)

This is a barebones Huggingface AutoModel compatible implementation of Boltz2 focused on fast inference workflows.

The implementation is located in boltz_automodel/ and exposes:

• Boltz2Config
• Boltz2Model
• predict_structure(amino_acid_sequence, ...)
• save_as_cif(structure_output, output_path, ...)

Design goals

• Inference-only (no training hooks, no Lightning trainer usage).
• Lightweight runtime around torch + transformers (plus numpy ).
• AutoModel remote-code compatibility via trust_remote_code=True .
• Confidence outputs included in prediction outputs ( plddt , ptm , iptm , and derived confidence score when available).

Runtime note

This implementation is self-contained inside boltz_automodel/ and does not require the original cloned boltz package at runtime.

Use with transformers

Load from an exported directory

import torch
from transformers import AutoModel

model = AutoModel.from_pretrained(
    "Synthyra/Boltz2",
    trust_remote_code=True,
    dtype=torch.float32,
).eval()

Predict structure from sequence

out = model.predict_structure(
    amino_acid_sequence="MSTNPKPQRKTKRNTNRRPQDVKFPGG",
    recycling_steps=3,
    num_sampling_steps=200,
    diffusion_samples=1,
)

print(out.sample_atom_coords.shape)
print(None if out.plddt is None else out.plddt.shape)

Save CIF

model.save_as_cif(out, "prediction.cif")

Convert Boltz checkpoint to HF export

Use:

py -m boltz_automodel.get_boltz2_weights --checkpoint_path boltz_automodel/weights/boltz2_conf.ckpt --output_dir boltz2_automodel_export

The export directory contains:

• config.json
• pytorch_model.bin
• modeling_boltz2.py
• minimal_featurizer.py
• minimal_structures.py
• cif_writer.py
• vb_*.py (self-contained vendored Boltz2 inference modules/constants)

Output object fields

predict_structure(...) returns Boltz2StructureOutput with:

• sample_atom_coords
• atom_pad_mask
• plddt
• complex_plddt
• ptm
• iptm
• confidence_score (derived when available)
• raw_output

Limitations

• Current featurization path is protein-only and minimal.
• This implementation is meant for practical inference and export workflows, not full Boltz training parity.

Docker-first compliance testing

Build the container at repo root:

docker build -t fastplms-test -f Dockerfile .

Launch a test shell:

docker run --rm --gpus all -it -v ${PWD}:/workspace fastplms-test bash

Inside the container, run Boltz2 compliance against pip boltz :

python -m test_scripts.run_boltz2_compliance --device cuda --dtype float32 --seed 42 --num-sequences 3 --recycling-steps 3 --num-sampling-steps 200 --diffusion-samples 1

Artifacts are written to test_scripts/results/<timestamp>/boltz2_compliance/ by default:

• metrics.json
• metrics.csv
• summary.txt