Add video to axolotl quick start (#10870)

* Add video to axolotl quick start.
* Fix wget url.

docs/readthedocs/source/_templates/sidebar_quicklinks.html

@@ -56,7 +56,7 @@
                         <a href="doc/LLM/Quickstart/fastchat_quickstart.html">Run IPEX-LLM Serving with FastChat</a>
                     </li>
                     <li>
-                        <a href="doc/LLM/Quickstart/axolotl_quickstart.html">Finetune LLM with Axolotl on Intel GPU without coding</a>
+                        <a href="doc/LLM/Quickstart/axolotl_quickstart.html">Finetune LLM with Axolotl on Intel GPU</a>
                     </li>
                 </ul>
             </li>

docs/readthedocs/source/doc/LLM/Quickstart/axolotl_quickstart.md

@@ -1,9 +1,11 @@
-# Finetune LLM with Axolotl on Intel GPU without coding
+# Finetune LLM with Axolotl on Intel GPU
 
 [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) is a popular tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures. You can now use [`ipex-llm`](https://github.com/intel-analytics/ipex-llm) as an accelerated backend for `Axolotl` running on Intel **GPU** *(e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max)*.
 
 See the demo of finetuning LLaMA2-7B on Intel Arc GPU below.
 
+<video src="https://llm-assets.readthedocs.io/en/latest/_images/axolotl-qlora-linux-arc.mp4" width="100%" controls></video>
+
 ## Quickstart
 
 ### 0. Prerequisites
@@ -37,8 +39,8 @@ pip install transformers==4.36.0
 # to avoid https://github.com/OpenAccess-AI-Collective/axolotl/issues/1544
 pip install datasets==2.15.0
 # prepare axolotl entrypoints
-wget https://github.com/intel-analytics/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/axolotl/finetune.py
-wget https://github.com/intel-analytics/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/axolotl/train.py
+wget https://raw.githubusercontent.com/intel-analytics/ipex-llm/main/python/llm/example/GPU/LLM-Finetuning/axolotl/finetune.py
+wget https://raw.githubusercontent.com/intel-analytics/ipex-llm/main/python/llm/example/GPU/LLM-Finetuning/axolotl/train.py
 ```
 
 **After the installation, you should have created a conda environment, named `axolotl` for instance, for running `Axolotl` commands with IPEX-LLM.**
@@ -105,7 +107,7 @@ After finishing accelerate config, check if `use_cpu` is disabled (i.e., `use_cp
 Prepare `lora.yml` for Axolotl LoRA finetune. You can download a template from github.
 
 ```cmd
-wget https://github.com/intel-analytics/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/axolotl/lora.yml
+wget https://raw.githubusercontent.com/intel-analytics/ipex-llm/main/python/llm/example/GPU/LLM-Finetuning/axolotl/lora.yml
 ```
 
 **If you are using the offline model and dataset in local env**, please modify the model path and dataset path in `lora.yml`. Otherwise, keep them unchanged.
@@ -149,7 +151,7 @@ accelerate launch train.py lora.yml
 Prepare `lora.yml` for QLoRA finetune. You can download a template from github.
 
 ```cmd
-wget https://github.com/intel-analytics/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/axolotl/qlora.yml
+wget https://raw.githubusercontent.com/intel-analytics/ipex-llm/main/python/llm/example/GPU/LLM-Finetuning/axolotl/qlora.yml
 ```
 
 **If you are using the offline model and dataset in local env**, please modify the model path and dataset path in `qlora.yml`. Otherwise, keep them unchanged.

docs/readthedocs/source/doc/LLM/Quickstart/index.rst

@@ -21,7 +21,7 @@ This section includes efficient guide to show you how to:
 * `Run Ollama with IPEX-LLM on Intel GPU <./ollama_quickstart.html>`_
 * `Run Llama 3 on Intel GPU using llama.cpp and ollama with IPEX-LLM <./llama3_llamacpp_ollama_quickstart.html>`_
 * `Run IPEX-LLM Serving with FastChat <./fastchat_quickstart.html>`_
-* `Finetune LLM with Axolotl on Intel GPU without coding <./axolotl_quickstart.html>`_
+* `Finetune LLM with Axolotl on Intel GPU <./axolotl_quickstart.html>`_
 
 .. |bigdl_llm_migration_guide| replace:: ``bigdl-llm`` Migration Guide
 .. _bigdl_llm_migration_guide: bigdl_llm_migration.html