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: 2,335 Bytes
8752091 6b7efd8 8752091 6577ccb 8752091 6b7efd8 8752091 6577ccb 8752091 2ca20a1 8752091 6b7efd8 8752091 843a654 8752091 6b7efd8 8752091 6577ccb 8752091 6b7efd8 8752091 6b7efd8 8752091 6577ccb 8752091 2ca20a1 8752091 6b7efd8 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 | """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",
]
|