add readme

README.md

@@ -13,9 +13,9 @@ library_name: transformers
 <div align="center">
 
   <br>
-  <h1>  InnoMegrez2 </h1>
+  <h1>  InnoMegrez2-Preview </h1>
 
-  <a href="https://github.com/sii-research/InnoMegrez2">
+  <a href="https://github.com/sii-research/InnoMegrez2-Preview">
     <b>🔗 Github</b>
   </a> &nbsp;|&nbsp;
   <a href="https://github.com/sii-research/InnoMegrez2/blob/main/docs/tech_report.pdf">
@@ -33,7 +33,7 @@ library_name: transformers
 
 ## Introduction
 
-InnoMegrez2 is a device native large language model. Megrez2 takes advantages of both the accuracy of Mixture-of-Experts (MoE) architecture and the compact size of Dense models. This preview model was trained on 5T Tokens of data. The official release, with larger training data and better reasoning and agent capabilities, will come later this year.
+InnoMegrez2-Preview is a device native large language model. Megrez2 takes advantages of both the accuracy of Mixture-of-Experts (MoE) architecture and the compact size of Dense models. This preview model was trained on 5T Tokens of data. The official release, with larger training data and better reasoning and agent capabilities, will come later this year.
 
 ## Model Card
 
@@ -62,7 +62,7 @@ InnoMegrez2 is a device native large language model. Megrez2 takes advantages of
 
 ## Performance
 
-We evaluated InnoMegrez2 using the open-source evaluation tool [OpenCompass](https://github.com/open-compass/opencompass) on several important benchmarks. Some of the evaluation results are shown in the table below.
+We evaluated InnoMegrez2-Preview using the open-source evaluation tool [OpenCompass](https://github.com/open-compass/opencompass) on several important benchmarks. Some of the evaluation results are shown in the table below.
 
 <div align="center">
 <table>
@@ -70,7 +70,7 @@ We evaluated InnoMegrez2 using the open-source evaluation tool [OpenCompass](htt
 <tr>
 <th align="center">Benchmark</th>
 <th align="center">Metric</th>
-<th align="center"><sup>InnoMegrez2</th>
+<th align="center"><sup>InnoMegrez2-Preview</th>
 <th align="center"><sup>Qwen2.5-3B</sup></th>
 <th align="center"><sup>Qwen2.5-7B</sup></th>
 <th align="center"><sup>Qwen3-4B</sup></th>
@@ -210,7 +210,7 @@ The following contains a code snippet illustrating how to use the model generate
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-path = "sii-research/InnoMegrez2"
+path = "sii-research/InnoMegrez2-Preview"
 device = "cuda"
 
 tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
@@ -240,7 +240,7 @@ print(responses)
 
 ## How to Deploy 
 
-InnoMegrez2 support using `vLLM` and `SGLang` as inference backends. For more information, please visit the [gitHub repository](https://github.com/sii-research/InnoMegrez2).
+InnoMegrez2-Preview support using `vLLM` and `SGLang` as inference backends. For more information, please visit the [gitHub repository](https://github.com/sii-research/InnoMegrez2).
 
 ## Best Practice