Update README.md

README.md

@@ -8,10 +8,11 @@ widget:
       text: "Hello! How can I help you today?"
 pipeline_tag: text-generation
 ---
-
 <!-- markdownlint-disable first-line-h1 -->
 <!-- markdownlint-disable html -->
-![](./assets/imgs/orion_start.PNG)
+<div align="center">
+  <img src="./assets/imgs/orion_start.PNG" alt="logo" width="50%" />
+</div>
 
 <div align="center">
 <h1>
@@ -26,7 +27,7 @@ pipeline_tag: text-generation
     <p>
         <b>🌐English</b> |
         <a href="https://huggingface.co/OrionStarAI/Orion-14B-Base-Int4/blob/main/README_zh.md">🇨🇳中文</a><br><br>
-        🤗 <a href="https://huggingface.co/OrionStarAI" target="_blank">HuggingFace Mainpage</a> | 🤖 <a href="https://modelscope.cn/organization/OrionStarAI" target="_blank">ModelScope Mainpage</a><br>🎬 <a href="https://huggingface.co/spaces/OrionStarAI/Orion-14B-App-Demo" target="_blank">HuggingFace Demo</a> | 🎫 <a href="https://modelscope.cn/studios/OrionStarAI/Orion-14B-App-Demo/summary" target="_blank">ModelScope Demo</a>
+        🤗 <a href="https://huggingface.co/OrionStarAI" target="_blank">HuggingFace Mainpage</a> | 🤖 <a href="https://modelscope.cn/organization/OrionStarAI" target="_blank">ModelScope Mainpage</a><br>🎬 <a href="https://huggingface.co/spaces/OrionStarAI/Orion-14B-App-Demo" target="_blank">HuggingFace Demo</a> | 🎫 <a href="https://modelscope.cn/studios/OrionStarAI/Orion-14B-App-Demo/summary" target="_blank">ModelScope Demo</a><br>😺 <a href="https://github.com/OrionStarAI/Orion" target="_blank">GitHub</a><br>📖 <a href="https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf" target="_blank">Tech Report</a>
     <p>
 </h4>
 
@@ -40,20 +41,34 @@ pipeline_tag: text-generation
 - [🔗 Model Download](#model-download)
 - [🔖 Model Benchmark](#model-benchmark)
 - [📊 Model Inference](#model-inference)
-- [🥇 Company Introduction](#company-introduction)
 - [📜 Declarations & License](#declarations-license)
+- [🥇 Company Introduction](#company-introduction)
 
 # 1. Model Introduction
 
-- Orion-14B-Base-Int4 is quantized using awq from Orion-14B-Base while reducing model size by 70% and improving inference speed by 30%, with performance loss less than 1%.
- <div align="center">
-  <img src="./assets/imgs/model_cap_en.png" alt="model_cap_en" width="50%" />
-</div>
+- Orion-14B series models are open-source multilingual large language models trained from scratch by OrionStarAI.  The base model is trained on 2.5T multilingual corpus, including Chinese, English, Japanese, Korean, etc, and it exhibits superior performance in these languages.  For details, please refer to [tech report](https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf).
+
+- The Orion-14B series models exhibit the following features:
+  - Among models with 20B-parameter scale level, Orion-14B-Base model shows outstanding performance in comprehensive evaluations.
+  - Strong multilingual capabilities, significantly outperforming in Japanese and Korean testsets.
+  - The fine-tuned models demonstrate strong adaptability, excelling in human-annotated blind tests.
+  - The long-chat version supports extremely long texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k.
+  - The quantized versions reduce model size by 70%, improve inference speed by 30%, with performance loss less than 1%.
+ <table style="border-collapse: collapse; width: 100%;">
+   <tr>
+     <td style="border: none; padding: 10px; box-sizing: border-box;">
+       <img src="./assets/imgs/opencompass_en.png" alt="opencompass" style="width: 100%; height: auto;">
+     </td>
+     <td style="border: none; padding: 10px; box-sizing: border-box;">
+       <img src="./assets/imgs/model_cap_en.png" alt="modelcap" style="width: 100%; height: auto;">
+     </td>
+   </tr>
+ </table>
 
 - Orion-14B series models including:
   - **Orion-14B-Base:**  A multilingual large language foundational model with 14 billion parameters, pretrained on a diverse dataset of 2.5 trillion tokens.
   - **Orion-14B-Chat:**  A chat-model fine-tuned on a high-quality corpus aims to provide an excellence interactive experience for users in the large model community.
-  - **Orion-14B-LongChat:**  This model is optimized for long context lengths more than 200k tokens and demonstrates performance comparable to proprietary models on long context evaluation sets.
+  - **Orion-14B-LongChat:**  The long-context version excels at handling extremely lengthy texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k.
   - **Orion-14B-Chat-RAG:**  A chat-model fine-tuned on a custom retrieval augmented generation dataset, achieving superior performance in retrieval augmented generation tasks.
   - **Orion-14B-Chat-Plugin:**  A chat-model specifically tailored for plugin and function calling tasks, ideal for agent-related scenarios where the LLM acts as a plugin and function call system.
   - **Orion-14B-Base-Int4:**  A quantized base model utilizing 4-bit integer weights. It significantly reduces the model size by 70% and increases the inference speed by 30% while incurring a minimal performance loss of only 1%.
@@ -321,7 +336,7 @@ Truly Useful Robots", OrionStar empowers more people through AI technology.
 
 **The core strengths of OrionStar lies in possessing end-to-end AI application capabilities,** including big data preprocessing, large model pretraining, fine-tuning, prompt engineering, agent, etc.  With comprehensive end-to-end model training capabilities, including systematic data processing workflows and the parallel model training capability of hundreds of GPUs, it has been successfully applied in various industry scenarios such as government affairs, cloud services, international e-commerce, and fast-moving consumer goods.
 
-Companies with demands for deploying large-scale model applications are welcome to contact us.
+Companies with demands for deploying large-scale model applications are welcome to contact us.<br>
 **Enquiry Hotline: 400-898-7779**<br>
 **E-mail: [email protected]**