Documentation Cleanup (#1644)

* Start cleaning up docs

* Remove page

* Minor update

* correction

* Minor doc revisions

* Update installation.mdx

* Update _toctree.yml

Author: matthewdouglas

docs/source/_toctree.yml

@@ -2,18 +2,15 @@
   sections:
   - local: index
     title: bitsandbytes
-  - local: quickstart
-    title: Quickstart
   - local: installation
     title: Installation
-- title: Guides
+  - local: quickstart
+    title: Quickstart
+
+- title: Usage Guides
   sections:
   - local: optimizers
     title: 8-bit optimizers
-  - local: algorithms
-    title: Algorithms
-  - local: non_cuda_backends
-    title: Non-CUDA compute backends
   - local: fsdp_qlora
     title: FSDP-QLoRA
   - local: integrations
@@ -56,7 +53,7 @@
       title: RMSprop
     - local: reference/optim/sgd
       title: SGD
-  - title: k-bit quantizers
+  - title: Modules
     sections:
     - local: reference/nn/linear8bit
       title: LLM.int8()

docs/source/algorithms.mdx

@@ -1,5 +1,4 @@
-# Contributors guidelines
-... still under construction ... (feel free to propose materials, `bitsandbytes` is a community project)
+# Contribution Guide
 
 ## Setup
 

docs/source/contributing.mdx

@@ -3,5 +3,3 @@
 Please submit your questions in [this Github Discussion thread](https://github.com/bitsandbytes-foundation/bitsandbytes/discussions/1013) if you feel that they will likely affect a lot of other users and that they haven't been sufficiently covered in the documentation.
 
 We'll pick the most generally applicable ones and post the QAs here or integrate them into the general documentation (also feel free to submit doc PRs, please).
-
-# ... under construction ...

docs/source/faqs.mdx

@@ -9,8 +9,6 @@ The `bitsandbytes.functional` API provides the low-level building blocks for the
 * For experimental or research purposes requiring non-standard quantization or performance optimizations.
 
 ## LLM.int8()
-[[autodoc]] functional.int8_double_quant
-
 [[autodoc]] functional.int8_linear_matmul
 
 [[autodoc]] functional.int8_mm_dequant
@@ -19,7 +17,6 @@ The `bitsandbytes.functional` API provides the low-level building blocks for the
 
 [[autodoc]] functional.int8_vectorwise_quant
 
-
 ## 4-bit
 [[autodoc]] functional.dequantize_4bit
 
@@ -49,5 +46,3 @@ For more details see [8-Bit Approximations for Parallelism in Deep Learning](htt
 
 ## Utility
 [[autodoc]] functional.get_ptr
-
-[[autodoc]] functional.is_on_gpu