From a7b1730ebb362cc82988cdaccb7a4e8b52ec495a Mon Sep 17 00:00:00 2001
From: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>
Date: Tue, 16 Apr 2024 17:09:18 +0800
Subject: [PATCH] [DOCS] readme update. (#18)

* Update dependabot.yml

* remove dependency.

* UPDATE INT8xINT2

* refactor the docs' link

* update int range

* Update installation and integration links in README.md
---
 README.md | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 7914482fe59f..8b5349dcbbed 100644
--- a/README.md
+++ b/README.md
@@ -68,16 +68,17 @@ We are continuously expanding the support matrix. If you have any specific requi
 
 ## Getting Started
 
-- [Installation](/docs/Installation.md):
-  To install BitBLAS, please checkout the document [installation](/docs/Installation.md). Also Make sure you already have the cuda toolkit (version >= 11) installed in the system. Or you can easily install from `pip install bitblas` in the root directory. 
+- [Installation](docs/Installation.md):
+  To install BitBLAS, please checkout the document [installation](docs/Installation.md). Also Make sure you already have the cuda toolkit (version >= 11) installed in the system. Or you can easily install from `pip install bitblas` in the root directory. 
 
-- [QuickStart](/docs/QuickStart.md): BitBLAS provides two Python APIs to perform mixed-precision matrix multiplication:
+- [QuickStart](docs/QuickStart.md): BitBLAS provides two Python APIs to perform mixed-precision matrix multiplication:
   - ```bitblas.Matmul``` implements the $W_{wdtype}A_{adtype}$ mixed-precision matrix multiplication of $C_{cdtype}[M, N] = A_{adtype}[M, K] \times W_{wdtype}[N, K]$.
   - ```bitblas.Linear``` is a PyTorch ```nn.Linear```-like module to support a Linear of mixed-precision.
 
-- [Integration](/integration/): Explore how BitBLAS seamlessly integrates with LLM deployment frameworks through our examples. Discover the ease of integrating BitBLAS with PyTorch, AutoGPTQ, and vLLM in the 3rd-party integration examples.
+- [Integration](integration/): Explore how BitBLAS seamlessly integrates with LLM deployment frameworks through our examples. Discover the ease of integrating BitBLAS with PyTorch, AutoGPTQ, and vLLM in the 3rd-party integration examples.
+
+- [Customization](docs/ExtendOperatorsWithDSL.md): BitBLAS supports implementing customized mixed-precision DNN operations rather than matrix multiplication with the flexible DSL (TIR Script).
 
-- [Customization](/docs/ExtendOperatorsWithDSL.md): BitBLAS supports implementing customized mixed-precision DNN operations rather than matrix multiplication with the flexible DSL (TIR Script).
 
 ## Contributing