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* Start generating return types * Finish tensor type hinting * Add `save_gguf` to `utils` * Typehint `quant-llama.py`
71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
# Generated content DO NOT EDIT
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
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from os import PathLike
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from candle.typing import _ArrayLike, Device
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from candle import Tensor, DType, QTensor
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@staticmethod
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def cuda_is_available() -> bool:
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"""
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Returns true if the 'cuda' backend is available.
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"""
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pass
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@staticmethod
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def get_num_threads() -> int:
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"""
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Returns the number of threads used by the candle.
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"""
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pass
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@staticmethod
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def has_accelerate() -> bool:
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"""
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Returns true if candle was compiled with 'accelerate' support.
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"""
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pass
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@staticmethod
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def has_mkl() -> bool:
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"""
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Returns true if candle was compiled with MKL support.
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"""
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pass
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@staticmethod
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def load_ggml(path: Union[str, PathLike]) -> Tuple[Dict[str, QTensor], Dict[str, Any], List[str]]:
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"""
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Load a GGML file. Returns a tuple of three objects: a dictionary mapping tensor names to tensors,
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a dictionary mapping hyperparameter names to hyperparameter values, and a vocabulary.
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"""
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pass
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@staticmethod
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def load_gguf(path: Union[str, PathLike]) -> Tuple[Dict[str, QTensor], Dict[str, Any]]:
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"""
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Loads a GGUF file. Returns a tuple of two dictionaries: the first maps tensor names to tensors,
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and the second maps metadata keys to metadata values.
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"""
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pass
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@staticmethod
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def load_safetensors(path: Union[str, PathLike]) -> Dict[str, Tensor]:
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"""
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Loads a safetensors file. Returns a dictionary mapping tensor names to tensors.
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"""
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pass
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@staticmethod
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def save_gguf(path: Union[str, PathLike], tensors: Dict[str, QTensor], metadata: Dict[str, Any]):
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"""
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Save quanitzed tensors and metadata to a GGUF file.
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"""
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pass
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@staticmethod
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def save_safetensors(path: Union[str, PathLike], tensors: Dict[str, Tensor]) -> None:
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"""
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Saves a dictionary of tensors to a safetensors file.
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"""
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pass
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