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---
library_name: "ofoldx"
tags:
- "biology"
- "biomolecular-design"
- "protein"
- "rna"
- "dna"
- "pipeline"
- "boltzgen-structure-small"
- "design-generation"
- "protein-design"
artifact_kind: "pipeline"
repo_id: "oteam/boltzgen-structure-small"
license: "mit"
base_model: "boltz-community/boltz-2"
datasets:
- "boltzgen/boltzgen1_train"
- "boltzgen/inference-data"
pipeline_tag: "other"
task: "design_generation"
model-index:
- name: "boltzgen-structure-small"
results:
[]
widget:
- pipeline_tag: "other"
task: "design_generation"
example_title: "Structure-conditioned generation"
text: "input_structure: seed_structure.cif\nnum_designs: 8"
input_format: "structure_path"
---
# boltzgen-structure-small
OFoldX `pipeline` artifact for biomolecular design generation, using the `boltzgen-structure-small` architecture.
## Disclaimer
This model card was generated by the OFoldX team for an OFoldX `pipeline` artifact.
The upstream model authors did not write this card unless explicitly stated otherwise.
OFoldX is pre-alpha research software. Check the source checkpoint, upstream release, and local validation
before using the artifact for scientific or operational decisions.
## Model Details
Small BoltzGen structure-conditioned generation checkpoint.
Converted small BoltzGen structure-trained checkpoint for structure-conditioned generation.
### Model Provenance
- **Upstream Project**: BoltzGen
- **Source Checkpoint**: `boltzgen1_structuretrained_small.ckpt`
- **Source Release**: [https://huggingface.co/boltzgen/boltzgen-1](https://huggingface.co/boltzgen/boltzgen-1)
- **Primary Paper**: [BoltzGen: Toward Universal Binder Design](https://doi.org/10.1101/2025.11.20.689494)
- **Upstream License**: MIT for upstream BoltzGen code
### Model Specification
| Field | Value |
| ----- | ----- |
| Repository | `oteam/boltzgen-structure-small` |
| Artifact Kind | `pipeline` |
| Task | `design_generation` |
| Architecture | `boltzgen-structure-small` |
| Entrypoint | `ofoldx.pipelines.design.DesignPipeline` |
| Source Checkpoint | `boltzgen1_structuretrained_small.ckpt` |
> [!NOTE]
> Source checkpoint: `boltzgen1_structuretrained_small.ckpt`.
### Links
- **Hub repository**: [oteam/boltzgen-structure-small](https://huggingface.co/oteam/boltzgen-structure-small)
- **Upstream paper**: [BoltzGen: Toward Universal Binder Design](https://doi.org/10.1101/2025.11.20.689494)
- **Upstream repository**: [BoltzGen](https://github.com/HannesStark/boltzgen)
- **Source checkpoint release**: [https://huggingface.co/boltzgen/boltzgen-1](https://huggingface.co/boltzgen/boltzgen-1)
- **Code**: [`ofoldx/pipelines/design.py`](https://github.com/OTeam-AI4S/OFoldX/tree/main/ofoldx/pipelines/design.py)
- **Project repository**: [https://github.com/OTeam-AI4S/OFoldX](https://github.com/OTeam-AI4S/OFoldX)
- **Issues**: [https://github.com/OTeam-AI4S/OFoldX/issues](https://github.com/OTeam-AI4S/OFoldX/issues)
## Usage
The artifact depends on the [`ofoldx`](https://github.com/OTeam-AI4S/OFoldX) library. Install it with pip:
```bash
pip install ofoldx
```
### Pipeline Usage
Load the artifact from `oteam/boltzgen-structure-small` with the OFoldX task pipeline. Use `AutoModel` or `AutoProcessor` only when you need lower-level control:
```python
from ofoldx.pipelines import Pipeline
pipeline = Pipeline.from_pretrained("oteam/boltzgen-structure-small")
```
When a matching processor is available, load it with `AutoProcessor.from_pretrained(...)` and pass the
processed batch to the model.
### Interface
- **Task**: `design_generation`
- **Artifact kind**: `pipeline`
- **Architecture**: `boltzgen-structure-small`
- **Runtime files**: `manifest.json`, `config.json`, and `model.safetensors` when present
## Training Details
OFoldX did not train these weights. This repository contains a converted checkpoint and OFoldX runtime
metadata for loading it.
### Training Data
BoltzGen builds on the Boltz-2 data pipeline. The public training release includes `targets.zip` and `msa.zip` in the `boltzgen/boltzgen1_train` dataset and molecule dictionaries in `boltzgen/inference-data`; this artifact corresponds to the released small structure-trained checkpoint.
### Training Procedure
OFoldX converts the released BoltzGen structure-trained checkpoint; it does not run BoltzGen training.
## Evaluation
OFoldX conversion reports and contract tests validate artifact structure and checkpoint loading. Task-level
scientific evaluation should be checked against the corresponding upstream model release or paper.
## Limitations
- This artifact is distributed for research use.
- Inputs must match the model-specific processor and expected biomolecular representation.
- OFoldX is pre-alpha, so APIs and artifact metadata may still change before a stable release.
## Citation
Please cite the upstream BoltzGen work for the source checkpoint. If OFoldX supports your work, please also cite or link the OFoldX project repository.
```bibtex
@article{stark2025boltzgen,
author = {Stark, Hannes and Faltings, Felix and Choi, MinGyu and Xie, Yuxin and Hur, Eunsu and O'Donnell, Timothy John and Bushuiev, Anton and Ucar, Talip and Passaro, Saro and Mao, Weian and others},
title = {BoltzGen: Toward Universal Binder Design},
year = {2025},
doi = {10.1101/2025.11.20.689494},
journal = {bioRxiv}
}
```
## Contact
Please use [OFoldX GitHub issues](https://github.com/OTeam-AI4S/OFoldX/issues) for questions or comments about this model card.
## License
The Hub `license` metadata, when present, reflects the source checkpoint or upstream project license. The OFoldX project license is not yet finalized.
The source checkpoint is associated with the upstream license noted above: MIT for upstream BoltzGen code. Review both OFoldX and upstream terms before redistribution or production use.