Feature Extraction
Transformers
Safetensors
fast_esmfold
protein-language-model
fastplms
custom_code
Instructions to use Synthyra/FastESMFold with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/FastESMFold with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Synthyra/FastESMFold", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Synthyra/FastESMFold", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download requirements.txt from Synthyra/FastESMFold: direct link, hf CLI and curl.
- Browser
- Download file 382 Bytes
-
https://huggingface.co/Synthyra/FastESMFold/resolve/main/requirements.txt
- Command line
-
hf download hf://Synthyra/FastESMFold/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/Synthyra/FastESMFold/resolve/main/requirements.txt
382 Bytes
| # Direct runtime dependencies for Synthyra/FastESMFold. | |
| # 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 | |