From 0004b4d1d74bdfbe441cfc83b5d5f6b9da743031 Mon Sep 17 00:00:00 2001
From: Daniel Han <danielhanchen@gmail.com>
Date: Thu, 30 Nov 2023 04:18:16 +1100
Subject: [PATCH] Update README.md

---
 README.md | 61 ++++++++++++++++++++++++++++++++-----------------------
 1 file changed, 36 insertions(+), 25 deletions(-)

diff --git a/README.md b/README.md
index 22e0400d1a..57a2666bde 100644
--- a/README.md
+++ b/README.md
@@ -1,34 +1,45 @@
 # Unsloth
 2x faster 50% less memory LLM finetuning on a single GPU.
 
-`!pip install "unsloth[cu118] @ git+https://github.com/unslothai/unsloth.git"`
-`!pip install "unsloth[cu121] @ git+https://github.com/unslothai/unsloth.git"`
+# Installation Instructions
+Unsloth currently only supports Linux* and Pytorch >= 2.1.
 
+1. Find your CUDA version via
+```
+import torch; torch.version.cuda
+```
+2. For CUDA 11.8:
+```
+pip install "unsloth[cu118] @ git+https://github.com/unslothai/unsloth.git"
+```
+3. For CUDA 12.1:
+```
+pip install "unsloth[cu121] @ git+https://github.com/unslothai/unsloth.git"
+```
 
-### Google Colab examples
-1. [Unsloth fast finetuning example](https://colab.research.google.com/drive/1oW55fBmwzCOrBVX66RcpptL3a99qWBxb?usp=sharing)
-2. [Original slow finetuning example](https://colab.research.google.com/drive/1c7zxdLHaLJ9R9YTZ74y4tUERvS-kySyA?usp=sharing)
+To update Pytorch to 2.1:
+```
+conda install cudatoolkit xformers bitsandbytes pytorch pytorch-cuda=12.1 \
+  -c pytorch -c nvidia -c xformers -c conda-forge -y
+```
+or
+```
+pip install --upgrade --force-reinstall --no-cache-dir torch triton \
+  --index-url https://download.pytorch.org/whl/cu121
+```
+Change `cu121` to `cu118` for CUDA version 11.8 or 12.1. Go to https://pytorch.org/ to learn more.
 
-### Installation instructions
-In Google Colab:
+Then install Unsloth.
+
+For Google Colab and Kaggle instances:
+1. Try our Colab example:
+2. Try our Kaggle example:
+
+# Future Milestones
+
+# Troubleshooting
+1. Sometimes `bitsandbytes` or `xformers` does not link properly. Try running:
 ```
 !ldconfig /usr/lib64-nvidia
-!pip install xformers --index-url https://download.pytorch.org/whl/cu118
-!pip install git+https://github.com/danielhanchen/unsloth.git
 ```
-`!ldconfig /usr/lib64-nvidia` is necessary (for now) to link CUDA with Python. Possibly a Google Colab linking bug.
-
-For general installations:
-1. Install Xformers *OR* Flash Attention. Choose 1. Old GPUs use Xformers. New use Flash Attention.
-2. For Xformers, find your Pytorch CUDA version via `torch.version.cuda` or `nvidia-smi`.
-   * If you have Conda, `conda install xformers -c xformers`
-   * If you have CUDA 11.8, `pip install xformers --index-url https://download.pytorch.org/whl/cu118`
-   * If you have CUDA 12.1, `pip install xformers --index-url https://download.pytorch.org/whl/cu121`
-   * Go to https://github.com/facebookresearch/xformers for other issues.
-   * You must have Pytorch 2.1 installed for Xformers. If not, try Flash Attention.
-   * Xformers supports all GPUs (Tesla T4 etc).
-3. For Flash Attention, you must have a Ampere, Ada, Hopper GPU (A100, RTX 3090, RTX 4090, H100).
-   * Install Flash Attention via `pip uninstall -y ninja && pip install ninja` then `pip install flash-attn --no-build-isolation`.
-   * Xformers has native support for Flash Attention, so technically installing Xformers is enough.
-4. Then install Unsloth:
-   `pip install git+https://github.com/danielhanchen/unsloth.git`
+2. Windows is not supported as of yet - we rely on Xformers and Triton support, so until both packages support Windows officially, Unsloth will then support Windows.