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fix docstring

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Manuel de Prada Corral 2025-06-29 10:47:40 +02:00
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commit 46ca0da436
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src/transformers/cache_utils.py
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@ -1314,25 +1314,6 @@ class SlidingWindowCache(Cache):
55, 56, 57, 58, 59, 60, 61, 62, 63, 0])
We overwrite the cache using these, then we always write at cache_position (clamped to `sliding_window`)
Parameters:
config (`PretrainedConfig`):
The configuration file defining the shape-related attributes required to initialize the static cache.
max_batch_size (`int`):
The maximum batch size with which the model will be used. Note that a new instance must be instantiated if a
smaller batch size is used.
max_cache_len (`int`, *optional*):
The maximum sequence length with which the model will be used.
device (`torch.device` or `str`, *optional*):
The device on which the cache should be initialized. If you're using more than 1 computation device, you
should pass the `layer_device_map` argument instead.
dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
The default `dtype` to use when initializing the layer.
layer_device_map (`Optional[dict[int, Union[str, torch.device, int]]]]`, *optional*):
Mapping between the layers and its device. This is required when you are manually initializing the cache
and the model is split between different gpus. You can know which layers mapped to which device by
checking the associated device_map: `model.hf_device_map`.
Example:
```python