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/attention/__init__.py from Synthyra/FastESMFold: direct link, hf CLI and curl.
- Browser
- Download file 2.34 kB
-
https://huggingface.co/Synthyra/FastESMFold/resolve/main/fastplms/attention/__init__.py
- Command line
-
hf download hf://Synthyra/FastESMFold/fastplms/attention/__init__.py
-
curl -L -o __init__.py https://huggingface.co/Synthyra/FastESMFold/resolve/main/fastplms/attention/__init__.py
2.34 kB
| """Shared attention backends, masks, and optional optimized kernels.""" | |
| from ._auto import ( | |
| AUTO_ATTENTION, | |
| AttentionCandidate, | |
| AttentionExecutionContext, | |
| AttentionResolution, | |
| ) | |
| from ._core import ( | |
| LEGACY_CHECKPOINT_ATTENTION_BACKENDS, | |
| VALID_ATTENTION_BACKENDS, | |
| AttentionBackend, | |
| BlockMask, | |
| FlashPaddingLayout, | |
| _ensure_flash_kernels_loaded, | |
| _get_flex_attention_fn, | |
| _get_flex_block_mask, | |
| _kernels_flash_forward, | |
| _kernels_flash_varlen_forward, | |
| _unpad_input, | |
| bool_to_additive_mask, | |
| canonical_checkpoint_attention_backend, | |
| clear_flex_attention_caches, | |
| create_block_mask, | |
| flash_kernel_unsupported_reason, | |
| flex_attention, | |
| get_attention_mask, | |
| get_attn_implementation, | |
| get_flash_padding_layout, | |
| index_first_axis, | |
| index_put_first_axis, | |
| kernels_flash_attention_func, | |
| pad_input, | |
| resolve_attention_backend, | |
| resolve_attention_backend_for_call, | |
| set_config_attn_implementation, | |
| warn_attention_backend_fallback, | |
| ) | |
| from .interfaces import ( | |
| FASTPLMS_ATTENTION_FUNCTIONS, | |
| FASTPLMS_ATTENTION_MASKS, | |
| FastPLMsAttentionMixin, | |
| validate_transformers_attention_interfaces, | |
| ) | |
| __all__ = [ | |
| "AUTO_ATTENTION", | |
| "FASTPLMS_ATTENTION_FUNCTIONS", | |
| "FASTPLMS_ATTENTION_MASKS", | |
| "LEGACY_CHECKPOINT_ATTENTION_BACKENDS", | |
| "VALID_ATTENTION_BACKENDS", | |
| "AttentionBackend", | |
| "AttentionCandidate", | |
| "AttentionExecutionContext", | |
| "AttentionResolution", | |
| "BlockMask", | |
| "FastPLMsAttentionMixin", | |
| "FlashPaddingLayout", | |
| "_ensure_flash_kernels_loaded", | |
| "_get_flex_attention_fn", | |
| "_get_flex_block_mask", | |
| "_kernels_flash_forward", | |
| "_kernels_flash_varlen_forward", | |
| "_unpad_input", | |
| "bool_to_additive_mask", | |
| "canonical_checkpoint_attention_backend", | |
| "clear_flex_attention_caches", | |
| "create_block_mask", | |
| "flash_kernel_unsupported_reason", | |
| "flex_attention", | |
| "get_attention_mask", | |
| "get_attn_implementation", | |
| "get_flash_padding_layout", | |
| "index_first_axis", | |
| "index_put_first_axis", | |
| "kernels_flash_attention_func", | |
| "pad_input", | |
| "resolve_attention_backend", | |
| "resolve_attention_backend_for_call", | |
| "set_config_attn_implementation", | |
| "validate_transformers_attention_interfaces", | |
| "warn_attention_backend_fallback", | |
| ] | |