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34 changes: 15 additions & 19 deletions docs/source/_toctree.yml
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- title: Get started
sections:
- local: index
title: Index
title: bitsandbytes
- local: quickstart
title: Quickstart
- local: installation
title: Installation
- title: Features & Integrations
- title: Guides
sections:
- local: quantization
title: Quantization
- local: optimizers
title: Optimizers
- local: integrations
title: Integrations
title: 8-bit optimizers
- local: algorithms
title: Algorithms
- title: Support & Learning
- local: integrations
title: Integrations
- local: errors
title: Troubleshoot
- local: contributing
title: Contribute
- local: faqs
title: FAQs
- title: Explanation
sections:
- local: resources
title: Papers, resources & how to cite
- local: errors
title: Errors & Solutions
- local: nonpytorchcuda
title: Non-PyTorch CUDA
- local: compiling
title: Compilation from Source (extended)
- local: faqs
title: FAQs (Frequently Asked Questions)
- title: Contributors Guidelines
- title: API reference
sections:
- local: contributing
title: Contributing
- local: quantization
title: Quantization
50 changes: 0 additions & 50 deletions docs/source/compiling.mdx

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2 changes: 1 addition & 1 deletion docs/source/errors.mdx
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# Errors & Solutions
# Troubleshoot

## No kernel image available

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86 changes: 65 additions & 21 deletions docs/source/installation.mdx
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# Installation

Note currently `bitsandbytes` is only supported on CUDA GPU hardwares, support for AMD GPUs and M1 chips (MacOS) is coming soon.
bitsandbytes is only supported on CUDA GPUs for CUDA versions **10.2 - 12.0**. Select your operating system below to see the installation instructions.

<hfoptions id="OS system">
<hfoption id="Linux">

## Hardware requirements:
- LLM.int8(): NVIDIA Turing (RTX 20xx; T4) or Ampere GPU (RTX 30xx; A4-A100); (a GPU from 2018 or newer).
- 8-bit optimizers and quantization: NVIDIA Kepler GPU or newer (>=GTX 78X).
For Linux systems, make sure your hardware meets the following requirements to use bitsandbytes features.

Supported CUDA versions: 10.2 - 12.0 #TODO: check currently supported versions
| **Feature** | **Hardware requirement** |
|---|---|
| LLM.int8() | NVIDIA Turing (RTX 20 series, T4) or Ampere (RTX 30 series, A4-A100) GPUs |
| 8-bit optimizers/quantization | NVIDIA Kepler (GTX 780 or newer) |

## Linux
> [!WARNING]
> bitsandbytes >= 0.39.1 no longer includes Kepler binaries in pip installations. This requires manual compilation, and you should follow the general steps and use `cuda11x_nomatmul_kepler` for Kepler-targeted compilation.
### From Pypi
To install from PyPI.

```bash
pip install bitsandbytes
```

### From source
To compile from source, you need CMake >= **3.22.1** and Python >= **3.10** installed. Make sure you have a compiler installed to compile C++ (gcc, make, headers, etc.). For example, to install a compiler and CMake on Ubuntu:

You need CMake and Python installed. For Linux, make sure to install a compiler (`apt install build-essential`, for example).
```bash
apt-get install -y build-essential cmake
```

You should also install CUDA Toolkit by following the [NVIDIA CUDA Installation Guide for Linux](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) guide from NVIDIA.

Now to install the bitsandbytes package from source, run the following commands:

```bash
git clone https://github.com/TimDettmers/bitsandbytes.git && cd bitsandbytes/
Expand All @@ -30,17 +38,16 @@ cmake -DCOMPUTE_BACKEND=cuda -S .
make
pip install .
```
Note support for non-CUDA GPUs (e.g. AMD, Intel, Apple Silicon), is also coming soon.
For a more detailed compilation guide, head to the [dedicated page on the topic](./compiling)

> [!TIP]
> If you have multiple versions of CUDA installed or installed it in a non-standard location, please refer to CMake CUDA documentation for how to configure the CUDA compiler.
</hfoption>
<hfoption id="Windows">

## Windows

Windows builds require Visual Studio with C++ support, as well as the Cuda SDK installed.
Windows systems require Visual Studio with C++ support as well as an installation of the CUDA SDK.

Currently for Windows users, you need to build bitsandbytes from source:
You'll need to build bitsandbytes from source. To compile from source, you need CMake >= **3.22.1** and Python >= **3.10** installed. You should also install CUDA Toolkit by following the [CUDA Installation Guide for Windows](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html) guide from NVIDIA.

```bash
git clone https://github.com/TimDettmers/bitsandbytes.git && cd bitsandbytes/
Expand All @@ -52,15 +59,52 @@ python -m build --wheel

Big thanks to [wkpark](https://github.com/wkpark), [Jamezo97](https://github.com/Jamezo97), [rickardp](https://github.com/rickardp), [akx](https://github.com/akx) for their amazing contributions to make bitsandbytes compatible with Windows.

For a more detailed compilation guide, head to the [dedicated page on the topic](./compiling)

</hfoption>
<hfoption id="MacOS">

## MacOS

Mac support is still a work in progress. Please make sure to check out the [Apple Silicon implementation coordination issue](https://github.com/TimDettmers/bitsandbytes/issues/1020) to get notified about the discussions and progress with respect to MacOS integration.
> [!TIP]
> MacOS support is still a work in progress! Subscribe to this [issue](https://github.com/TimDettmers/bitsandbytes/issues/1020) to get notified about discussions and to track the integration progress.
</hfoption>

</hfoptions>

## PyTorch CUDA versions

Some bitsandbytes features may need a newer CUDA version than the one currently supported by PyTorch binaries from Conda and pip. In this case, you should follow these instructions to load a precompiled bitsandbytes binary.

1. Determine the path of the CUDA version you want to use. Common paths include:

* `/usr/local/cuda`
* `/usr/local/cuda-XX.X` where `XX.X` is the CUDA version number

Then locally install the CUDA version you need with this script from bitsandbytes:

```bash
wget https://raw.githubusercontent.com/TimDettmers/bitsandbytes/main/install_cuda.sh
# Syntax cuda_install CUDA_VERSION INSTALL_PREFIX EXPORT_TO_BASH
# CUDA_VERSION in {110, 111, 112, 113, 114, 115, 116, 117, 118, 120, 121, 122}
# EXPORT_TO_BASH in {0, 1} with 0=False and 1=True

# For example, the following installs CUDA 11.7 to ~/local/cuda-11.7 and exports the path to your .bashrc

bash cuda_install.sh 117 ~/local 1
```

2. Set the environment variables `BNB_CUDA_VERSION` and `LD_LIBRARY_PATH` by manually overriding the CUDA version installed by PyTorch.

> [!TIP]
> It is recommended to add the following lines to the `.bashrc` file to make them permanent.
```bash
export BNB_CUDA_VERSION=<VERSION>
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<PATH>
```

For example, to use a local install path:

```bash
export BNB_CUDA_VERSION=117
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/tim/local/cuda-11.7
```

3. Now when you launch bitsandbytes with these environment variables, the PyTorch CUDA version is overridden by the new CUDA version (in this example, version 11.7) and a different bitsandbytes library is loaded.
6 changes: 4 additions & 2 deletions docs/source/integrations.mdx
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Expand Up @@ -6,8 +6,10 @@ Please review the [bitsandbytes section in the Accelerate docs](https://huggingf

Details about the BitsAndBytesConfig can be found [here](https://huggingface.co/docs/transformers/v4.37.2/en/main_classes/quantization#transformers.BitsAndBytesConfig).

## Beware: bf16 is optional compute data type
If your hardware supports it, `bf16` is the optimal compute dtype. The default is `float32` for backward compatibility and numerical stability. `float16` often leads to numerical instabilities, but `bfloat16` provides the benefits of both worlds: numerical stability and significant computation speedup. Therefore, be sure to check if your hardware supports `bf16` and configure it using the `bnb_4bit_compute_dtype` parameter in BitsAndBytesConfig:
> [!WARNING]
> **Beware: bf16 is the optimal compute data type!**
>
> If your hardware supports it, `bf16` is the optimal compute dtype. The default is `float32` for backward compatibility and numerical stability. `float16` often leads to numerical instabilities, but `bfloat16` provides the benefits of both worlds: numerical stability equivalent to float32, but combined with the memory footprint and significant computation speedup of a 16-bit data type. Therefore, be sure to check if your hardware supports `bf16` and configure it using the `bnb_4bit_compute_dtype` parameter in BitsAndBytesConfig:
```py
import torch
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46 changes: 0 additions & 46 deletions docs/source/nonpytorchcuda.mdx

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