Feature Extraction
Transformers
PyTorch
Safetensors
boltz2_automodel
protein-language-model
fastplms
custom_code
Instructions to use Synthyra/Boltz2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/Boltz2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Synthyra/Boltz2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Synthyra/Boltz2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download requirements.txt from Synthyra/Boltz2: direct link, hf CLI and curl.
- Browser
- Download file 377 Bytes
-
https://huggingface.co/Synthyra/Boltz2/resolve/main/requirements.txt
- Command line
-
hf download hf://Synthyra/Boltz2/requirements.txt
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curl -L -o requirements.txt https://huggingface.co/Synthyra/Boltz2/resolve/main/requirements.txt
377 Bytes
| # Direct runtime dependencies for Synthyra/Boltz2. | |
| # FastPLMs source is embedded in this model repository. | |
| torch>=2.14 | |
| transformers>=5.17 | |
| huggingface-hub>=1.32 | |
| tokenizers>=0.23 | |
| safetensors>=0.8 | |
| numpy>=2.5 | |
| einops>=0.8 | |
| tqdm>=4.70 | |
| accelerate>=1.15 | |
| biopython>=1.88 | |
| biotite>=1.7 | |
| brotli>=1.2 | |
| msgpack>=1.2 | |
| msgpack-numpy>=0.4.8 | |
| omegaconf>=2.3 | |
| rdkit>=2026.3 | |
| scipy>=1.18 | |
| zstandard>=0.25 | |