Updated docs (#32) and changelog.

CHANGELOG.md

@@ -117,3 +117,16 @@ Features:
 
 Bug fixes:
  - fixed an issue where too many threads were created in blockwise quantization on the CPU for large tensors
+
+
+### 0.35.0
+
+#### CUDA 11.8 support and bug fixes
+
+Features:
+ - CUDA 11.8 support added and binaries added to the PyPI release.
+
+Bug fixes:
+ - fixed a bug where too long directory names would crash the CUDA SETUP #35 (thank you @tomaarsen)
+ - fixed a bug where CPU installations on Colab would run into an error  #34 (thank you @tomaarsen)
+ - fixed an issue where the default CUDA version with fast-DreamBooth was not supported #52

README.md

@@ -10,6 +10,8 @@ Resources:
 - [LLM.int8() Paper](https://arxiv.org/abs/2208.07339) -- [LLM.int8() Software Blog Post](https://huggingface.co/blog/hf-bitsandbytes-integration) -- [LLM.int8() Emergent Features Blog Post](https://timdettmers.com/2022/08/17/llm-int8-and-emergent-features/)
 
 ## TL;DR
+**Requirements**
+Linux distribution (Ubuntu, MacOS, etc.) + CUDA >= 10.0. LLM.int8() requires Turing or Ampere GPUs.
 **Installation**:
 ``pip install bitsandbytes``
 
@@ -52,6 +54,8 @@ Hardware requirements:
 
 Supported CUDA versions: 10.2 - 11.7
 
+The bitsandbytes library is currently only supported on Linux distributions. Windows is not supported at the moment.
+
 The requirements can best be fulfilled by installing pytorch via anaconda. You can install PyTorch by following the ["Get Started"](https://pytorch.org/get-started/locally/) instructions on the official website.
 
 ## Using bitsandbytes

setup.py

@@ -18,7 +18,7 @@ def read(fname):
 
 setup(
     name=f"bitsandbytes",
-    version=f"0.34.0",
+    version=f"0.35.0",
     author="Tim Dettmers",
     author_email="[email protected]",
     description="8-bit optimizers and matrix multiplication routines.",