run pre-commit hooks

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.15
+    rev: v0.2.0
     hooks:
       - id: ruff
         args:

bitsandbytes/nn/modules.py

@@ -226,11 +226,11 @@ def to(self, *args, **kwargs):
 
 class Linear4bit(nn.Linear):
     """
-    This class is the base module for the 4-bit quantization algorithm presented in [QLoRA](https://arxiv.org/abs/2305.14314). 
+    This class is the base module for the 4-bit quantization algorithm presented in [QLoRA](https://arxiv.org/abs/2305.14314).
     QLoRA 4-bit linear layers uses blockwise k-bit quantization under the hood, with the possibility of selecting various
     compute datatypes such as FP4 and NF4.
 
-    In order to quantize a linear layer one should first load the original fp16 / bf16 weights into 
+    In order to quantize a linear layer one should first load the original fp16 / bf16 weights into
     the Linear8bitLt module, then call `quantized_module.to("cuda")` to quantize the fp16 / bf16 weights.
 
     Example:
@@ -442,10 +442,10 @@ def maybe_rearrange_weight(state_dict, prefix, local_metadata, strict, missing_k
 
 class Linear8bitLt(nn.Linear):
     """
-    This class is the base module for the [LLM.int8()](https://arxiv.org/abs/2208.07339) algorithm. 
+    This class is the base module for the [LLM.int8()](https://arxiv.org/abs/2208.07339) algorithm.
     To read more about it, have a look at the paper.
 
-    In order to quantize a linear layer one should first load the original fp16 / bf16 weights into 
+    In order to quantize a linear layer one should first load the original fp16 / bf16 weights into
     the Linear8bitLt module, then call `int8_module.to("cuda")` to quantize the fp16 weights.
 
     Example:

docs/source/quantization.mdx

@@ -14,4 +14,4 @@ Below you will find the docstring of the quantization primitives exposed in bits
 
 ## StableEmbedding
 
-[[autodoc]] bitsandbytes.nn.StableEmbedding
+[[autodoc]] bitsandbytes.nn.StableEmbedding