Bootstrap Hosted Axolotl Docs w/Quarto (#1429)

* precommit

* mv styes.css

* fix links

docs/.gitignore

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+/.quarto/
+_site/

docs/config.qmd

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+---
+title: Config options
+description: A complete list of all configuration options.
+---
+
+```{python}
+#|echo: false
+#|output: asis
+import re
+# Regex pattern to match the YAML block including its code fence
+pattern = r'<details[^>]*id="all-yaml-options"[^>]*>.*?<summary>All yaml options.*?```yaml(.*?)```.*?</details>'
+
+with open('../README.md', 'r') as f:
+    doc = f.read()
+match = re.search(pattern, doc, re.DOTALL)
+print("```yaml", match.group(1).strip(), "```", sep="\n")
+```

docs/debugging.qmd

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-# Debugging Axolotl
+---
+title: Debugging
+description: How to debug Axolotl
+---
+
 
 This document provides some tips and tricks for debugging Axolotl.  It also provides an example configuration for debugging with VSCode.  A good debugging setup is essential to understanding how Axolotl code works behind the scenes.
 

docs/faq.md

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-# Axolotl FAQ's
-
-
-> The trainer stopped and hasn't progressed in several minutes.
-
-Usually an issue with the GPU's communicating with each other. See the [NCCL doc](../docs/nccl.md)
-
-> Exitcode -9
-
-This usually happens when you run out of system RAM.
-
-> Exitcode -7 while using deepspeed
-
-Try upgrading deepspeed w: `pip install -U deepspeed`
-
-> AttributeError: 'DummyOptim' object has no attribute 'step'
-
-You may be using deepspeed with single gpu. Please don't set `deepspeed:` in yaml or cli.

docs/faq.qmd

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+---
+title: FAQ
+description: Frequently asked questions
+---
+
+
+**Q: The trainer stopped and hasn't progressed in several minutes.**
+
+> A: Usually an issue with the GPUs communicating with each other. See the [NCCL doc](nccl.qmd)
+
+**Q: Exitcode -9**
+
+> A: This usually happens when you run out of system RAM.
+
+**Q: Exitcode -7 while using deepspeed**
+
+> A: Try upgrading deepspeed w: `pip install -U deepspeed`
+
+**Q: AttributeError: 'DummyOptim' object has no attribute 'step'**
+
+> A: You may be using deepspeed with single gpu. Please don't set `deepspeed:` in yaml or cli.

docs/fsdp_qlora.qmd

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-# FDSP + QLoRA
+---
+title: FDSP + QLoRA
+description: Use FSDP with QLoRA to fine-tune large LLMs on consumer GPUs.
+format:
+  html:
+    toc: true
+---
 
 ## Background
 

docs/input_output.qmd

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-# Template-free prompt construction with the `input_output` format
+---
+title: Template-free prompt construction
+description: "Template-free prompt construction with the `input_output` format"
+---
 
 <!-- TOC -->
 

docs/mac.qmd

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-# Mac M series support
+---
+title: Mac M-series
+description: Mac M-series support
+---
 
 Currently Axolotl on Mac is partially usable, many of the dependencies of Axolotl including Pytorch do not support MPS or have incomplete support.
 
 Current support:
+
 - [x] Support for all models
 - [x] Full training of models
 - [x] LoRA training

docs/multi-node.qmd

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-# Multi Node
+---
+title: Multi Node
+description: How to use Axolotl on multiple machines
+---
 
 You will need to create a configuration for accelerate, either by using `accelerate config` and follow the instructions or you can use one of the preset below:
 

docs/multipack.qmd

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-# Multipack (Sample Packing)
+---
+title: Multipack (Sample Packing)
+description: Multipack is a technique to pack multiple sequences into a single batch to increase training throughput.
+---
 
 ## Visualization of Multipack with Flash Attention
 

docs/nccl.qmd

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-# NCCL
+---
+title: NCCL
+description: Troubleshooting NCCL issues
+---
 
 NVIDIA NCCL is a library to facilitate and optimize multi-GPU communication operations, such as broadcast, all-gather, reduce, all-reduce, etc. Broadly, NCCL configuration is highly environment-specific and is configured via several [environment variables](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html). A common NCCL-related problem occurs when a long-running operation times out causing the training process to abort:
 

docs/rlhf.qmd

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-# RLHF (Beta)
+---
+title: "RLHF (Beta)"
+description: "Reinforcement Learning from Human Feedback is a method whereby a language model is optimized from data using human feedback."
+---
 
 ### Overview