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 fastplms/embeddings/__init__.py from Synthyra/FastESMFold: direct link, hf CLI and curl.
- Browser
- Download file 2.44 kB
-
https://huggingface.co/Synthyra/FastESMFold/resolve/main/fastplms/embeddings/__init__.py
- Command line
-
hf download hf://Synthyra/FastESMFold/fastplms/embeddings/__init__.py
-
curl -L -o __init__.py https://huggingface.co/Synthyra/FastESMFold/resolve/main/fastplms/embeddings/__init__.py
2.44 kB
| """Ordered, residue-aware protein embedding utilities.""" | |
| from .feature_runs import embed_into_features | |
| from .pooling import ( | |
| POOLING_NAMES, POOLING_SEMANTICS_TOKENS, TOKEN_POOLING_NAMES, Pooler, pagerank_weights, pool_token_rows, | |
| ) | |
| from .runner import ( | |
| EmbeddingMixin, | |
| embed_dataset, | |
| iter_fasta, | |
| parse_fasta, | |
| select_hidden_state_embeddings, | |
| ) | |
| from .storage import ( | |
| DEFAULT_SHARD_SIZE, | |
| append_sqlite_records, | |
| convert_legacy_sqlite, | |
| garbage_collect_safetensors_generations, | |
| initialize_sqlite_run, | |
| load_legacy_pth, | |
| load_result, | |
| load_safetensors_result, | |
| load_sqlite_result, | |
| save_result, | |
| save_safetensors_result, | |
| save_sqlite_result, | |
| tensor_sha256, | |
| update_sqlite_run_metadata, | |
| ) | |
| from .taps import ( | |
| HiddenTap, LayerAccumulator, ReducedTap, RowSelection, SparseResidueTap, StreamingTap, TapBatch, | |
| ) | |
| from .token_batches import BatchGeometry, TokenTapExecutor, plan_geometry_batches, plan_token_batches | |
| from .token_runs import embed_token_features | |
| from .tokens import ResidueVocabulary | |
| from .types import ( | |
| EmbeddingBatch, | |
| EmbeddingInput, | |
| EmbeddingRecord, | |
| EmbeddingResult, | |
| LazyTensorReference, | |
| TapRecord, | |
| TapResult, | |
| TapRunReceipt, | |
| TensorValue, | |
| ) | |
| __all__ = [ | |
| "DEFAULT_SHARD_SIZE", | |
| "POOLING_NAMES", | |
| "POOLING_SEMANTICS_TOKENS", | |
| "TOKEN_POOLING_NAMES", | |
| "BatchGeometry", | |
| "EmbeddingBatch", | |
| "EmbeddingInput", | |
| "EmbeddingMixin", | |
| "EmbeddingRecord", | |
| "EmbeddingResult", | |
| "HiddenTap", | |
| "LayerAccumulator", | |
| "LazyTensorReference", | |
| "Pooler", | |
| "ReducedTap", | |
| "ResidueVocabulary", | |
| "RowSelection", | |
| "SparseResidueTap", | |
| "StreamingTap", | |
| "TapBatch", | |
| "TapRecord", | |
| "TapResult", | |
| "TapRunReceipt", | |
| "TensorValue", | |
| "TokenTapExecutor", | |
| "append_sqlite_records", | |
| "convert_legacy_sqlite", | |
| "embed_dataset", | |
| "embed_into_features", | |
| "embed_token_features", | |
| "garbage_collect_safetensors_generations", | |
| "initialize_sqlite_run", | |
| "iter_fasta", | |
| "load_legacy_pth", | |
| "load_result", | |
| "load_safetensors_result", | |
| "load_sqlite_result", | |
| "pagerank_weights", | |
| "parse_fasta", | |
| "plan_geometry_batches", | |
| "plan_token_batches", | |
| "pool_token_rows", | |
| "save_result", | |
| "save_safetensors_result", | |
| "save_sqlite_result", | |
| "select_hidden_state_embeddings", | |
| "tensor_sha256", | |
| "update_sqlite_run_metadata", | |
| ] | |