From b389f339ec016cb83f0975c1c9cc0d7965e411f8 Mon Sep 17 00:00:00 2001
From: Dhruv Nair <dhruv.nair@gmail.com>
Date: Wed, 18 Dec 2024 18:32:36 +0530
Subject: [PATCH] Fix Doc links in GGUF and Quantization overview docs 
 (#10279)

* update

* Update docs/source/en/quantization/gguf.md

Co-authored-by: Aryan <aryan@huggingface.co>

---------

Co-authored-by: Aryan <aryan@huggingface.co>
---
 docs/source/en/quantization/gguf.md     | 4 ++--
 docs/source/en/quantization/overview.md | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/source/en/quantization/gguf.md b/docs/source/en/quantization/gguf.md
index dbcd1b1486..2ff2a92931 100644
--- a/docs/source/en/quantization/gguf.md
+++ b/docs/source/en/quantization/gguf.md
@@ -25,9 +25,9 @@ pip install -U gguf
 
 Since GGUF is a single file format, use [`~FromSingleFileMixin.from_single_file`] to load the model and pass in the [`GGUFQuantizationConfig`].
 
-When using GGUF checkpoints, the quantized weights remain in a low memory `dtype`(typically `torch.unint8`) and are dynamically dequantized and cast to the configured `compute_dtype` during each module's forward pass through the model. The `GGUFQuantizationConfig` allows you to set the `compute_dtype`. 
+When using GGUF checkpoints, the quantized weights remain in a low memory `dtype`(typically `torch.uint8`) and are dynamically dequantized and cast to the configured `compute_dtype` during each module's forward pass through the model. The `GGUFQuantizationConfig` allows you to set the `compute_dtype`.
 
-The functions used for dynamic dequantizatation are based on the great work done by [city96](https://github.com/city96/ComfyUI-GGUF), who created the Pytorch ports of the original (`numpy`)[https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/gguf/quants.py] implementation by [compilade](https://github.com/compilade).
+The functions used for dynamic dequantizatation are based on the great work done by [city96](https://github.com/city96/ComfyUI-GGUF), who created the Pytorch ports of the original [`numpy`](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/gguf/quants.py) implementation by [compilade](https://github.com/compilade).
 
 ```python
 import torch
diff --git a/docs/source/en/quantization/overview.md b/docs/source/en/quantization/overview.md
index 6c2df7514d..3eef5238f1 100644
--- a/docs/source/en/quantization/overview.md
+++ b/docs/source/en/quantization/overview.md
@@ -33,8 +33,8 @@ If you are new to the quantization field, we recommend you to check out these be
 ## When to use what?
 
 Diffusers currently supports the following quantization methods.
-- [BitsandBytes]()
-- [TorchAO]()
-- [GGUF]()
+- [BitsandBytes](./bitsandbytes.md)
+- [TorchAO](./torchao.md)
+- [GGUF](./gguf.md)
 
 [This resource](https://huggingface.co/docs/transformers/main/en/quantization/overview#when-to-use-what) provides a good overview of the pros and cons of different quantization techniques.