new additions

.pre-commit-config.yaml

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

bitsandbytes/nn/modules.py

@@ -222,9 +222,50 @@ 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). 
+    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 
+    the Linear8bitLt module, then call `quantized_module.to("cuda")` to quantize the fp16 / bf16 weights.
+
+    Example:
+
+    ```python
+    import torch
+    import torch.nn as nn
+
+    import bitsandbytes as bnb
+    from bnb.nn import Linear4bit
+
+    fp16_model = nn.Sequential(
+        nn.Linear(64, 64),
+        nn.Linear(64, 64)
+    )
 
+    quantized_model = nn.Sequential(
+        Linear4bit(64, 64),
+        Linear4bit(64, 64)
+    )
+
+    quantized_model.load_state_dict(fp16_model.state_dict())
+    quantized_model = quantized_model.to(0) # Quantization happens here
+    ```
+    """
     def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True, quant_type='fp4', quant_storage=torch.uint8, device=None):
         super().__init__(input_features, output_features, bias, device)
+        """
+        Initialize Linear4bit class.
+
+        Args:
+            input_features (`str`):
+                Number of input features of the linear layer.
+            output_features (`str`):
+                Number of output features of the linear layer.
+            bias (`bool`, defaults to `True`):
+                Whether the linear class uses the bias term as well.
+        """
         self.weight = Params4bit(self.weight.data, requires_grad=False, compress_statistics=compress_statistics, quant_type=quant_type, quant_storage=quant_storage, module=self)
         # self.persistent_buffers = []  # TODO consider as way to save quant state
         self.compute_dtype = compute_dtype
@@ -440,8 +481,7 @@ def __init__(self, input_features, output_features, bias=True, has_fp16_weights=
                 Number of output features of the linear layer.
             bias (`bool`, defaults to `True`):
                 Whether the linear class uses the bias term as well.
-        """
-
+        """ 
         assert not memory_efficient_backward, "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0"
         self.state = bnb.MatmulLtState()
         self.index = index

docs/source/quantization.mdx

@@ -8,4 +8,5 @@ Below you will find the docstring of the quantization primitives exposed in bits
 
 
 # Linear4bit (QLoRA)
-... TODO: to be filled out ...
+
+[[autodoc]] bitsandbytes.nn.Linear4bit