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
File size: 2,437 Bytes
8752091 2ca20a1 8752091 2ca20a1 8752091 2ca20a1 8752091 843a654 8752091 2ca20a1 8752091 2ca20a1 8752091 2ca20a1 8752091 2ca20a1 8752091 2ca20a1 8752091 2ca20a1 8752091 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | """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",
]
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