Replace links with ones from Wayback Machine (#2)

- Replace links with ones from Wayback Machine (33e01d6e96953be61b695385beaa1b28050caf31)


Co-authored-by: xzuyn <[email protected]>

README.md

@@ -26,7 +26,7 @@ and consistently outperforms all the existing state-of-the-art opensource models
 - WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size. 
 - WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models.
 
-For more  details of WizardLM-2 please read our [release blog post](https://wizardlm.github.io/WizardLM2) and  upcoming paper.
+For more  details of WizardLM-2 please read our [release blog post](https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/) and  upcoming paper.
 
 
 ## Model Details
@@ -37,7 +37,7 @@ For more  details of WizardLM-2 please read our [release blog post](https://wiza
 * **Base model**: [mistral-community/Mixtral-8x22B-v0.1](https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1)
 * **Parameters**: 141B
 * **Language(s)**: Multilingual
-* **Blog**: [Introducing WizardLM-2](https://wizardlm.github.io/WizardLM2)
+* **Blog**: [Introducing WizardLM-2](https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/)
 * **Repository**: [https://github.com/nlpxucan/WizardLM](https://github.com/nlpxucan/WizardLM)
 * **Paper**: WizardLM-2 (Upcoming)
 * **License**: Apache2.0
@@ -53,7 +53,7 @@ The WizardLM-2 8x22B even demonstrates highly competitive performance compared t
 Meanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales.
 
 <p align="center" width="100%">
-<a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/mtbench.png" alt="MTBench" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="https://web.archive.org/web/20240415175608im_/https://wizardlm.github.io/WizardLM2/static/images/mtbench.png" alt="MTBench" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 
@@ -67,7 +67,7 @@ We report the win:loss rate without tie:
 - WizardLM-2 7B is comparable with Qwen1.5-32B-Chat, and surpasses Qwen1.5-14B-Chat and Starling-LM-7B-beta.
 
 <p align="center" width="100%">
-<a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/winall.png" alt="Win" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="https://web.archive.org/web/20240415163303im_/https://wizardlm.github.io/WizardLM2/static/images/winall.png" alt="Win" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 
@@ -75,10 +75,10 @@ We report the win:loss rate without tie:
 
 
 ## Method Overview
-We built a **fully AI powered synthetic training system** to train WizardLM-2 models, please refer to our [blog](https://wizardlm.github.io/WizardLM2) for more details of this system.
+We built a **fully AI powered synthetic training system** to train WizardLM-2 models, please refer to our [blog](https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/) for more details of this system.
 
 <p align="center" width="100%">
-<a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/exp_1.png" alt="Method" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="https://web.archive.org/web/20240415163303im_/https://wizardlm.github.io/WizardLM2/static/images/exp_1.png" alt="Method" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
 </p>