Update README.md

README.md

@@ -1,3 +1,67 @@
----
-license: apache-2.0
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+---
+license: apache-2.0
+language:
+- ar
+- zh
+- en
+---
+
+# <b>AceGPT</b>
+
+AceGPT is a fully fine-tuned generative text model collection, particularly focused on the Arabic language domain. 
+This is the repository for the version 2 of the 32B-chat pre-trained model, developed based on Qwen1.5-32B.
+
+---
+## Model Details
+We have released the AceGPT family of large language models, which is a collection of fully fine-tuned generative text models, ranging from 7B to 70B parameters. Our models include two main categories: AceGPT and AceGPT-chat. AceGPT-chat is an optimized version specifically designed for dialogue applications. It is worth mentioning that our models have demonstrated superior performance compared to all currently available open-source Arabic dialogue models in multiple benchmark tests. Furthermore, in our human evaluations, our models have shown comparable satisfaction levels to some closed-source models, such as ChatGPT, in the Arabic language.
+## Model Developers 
+We are from the King Abdullah University of Science and Technology (KAUST), the Chinese University of Hong Kong, Shenzhen (CUHKSZ), the Shenzhen Research Institute of Big Data (SRIBD), and King AbdulAziz University (KAU).
+## Variations
+AceGPT families come in a range of parameter sizes —— 7B, 8B, 13B, 32B and 70B, each size of model has a base category and a -chat category.
+## Paper
+The paper can be accessed at [link](https://huggingface.co/FreedomIntelligence/AceGPT-v2-70B-Chat/blob/main/Alignment_at_Pre_training__a_Case_Study_of_Aligning_LLMs_in_Arabic.pdf).
+## Input 
+Models input text only.
+## Output
+Models output text only.
+## Model Evaluation Results
+
+Benchmark evaluations are conducted using accuracy or F1 scores as metrics, following the evaluation framework available at https://github.com/FreedomIntelligence/AceGPT/tree/main.
+([**ArabicMMLU**](https://github.com/mbzuai-nlp/ArabicMMLU) is assessed based on its source settings.)
+|                  | [MMLU (Huang et al. (2023))](https://github.com/FreedomIntelligence/AceGPT) | [ArabicMMLU](https://github.com/mbzuai-nlp/ArabicMMLU) | EXAMS | ACVA (clean) | ACVA (all) | Arabic BoolQ | Arabic ARC-C | Average |
+|------------------|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|
+| LLaMA2-7B-chat   | 13.78 | 33.40 | 13.05 | 20.99 | 21.80 | 34.92 | 23.72 | 21.09 |
+| Llama2-13B-chat | 8.92 | 36.12 | 16.11 | 35.12 | 35.71 | 54.13 | 27.47 | 30.51 |
+| Jais-13B-chat | 19.52 | 54.83 | 19.71 | 66.75 | 61.41 | 41.25 | 11.95 | 39.34 |
+| Phoenix-7b       | 29.72 | 44.74 | 31.93 | 43.80 | 41.86 | 66.70 | 33.53 | 41.75 |
+| AceGPT-7B-chat   | 30.69 | 36.31 | 33.73 | 53.87 | 53.07 | 60.70 | 38.05 | 43.77 |
+| Mistral-7B-Instruct-v0.2 | 27.93 | 41.44 | 21.56 | 64.56 | 63.47 | 60.18 | 35.67 | 44.97 |
+| AceGPT-13B-Chat | 35.59 | 52.61 | 38.72 | 70.82 | 70.21 | 66.85 | 44.20 | 54.14 |
+| Jais-30B-chat-v3 | 35.68 | 62.36 | 32.24 | 73.63 | 73.66 | 76.30 | 51.02 | 57.84 |
+| Jais-30B-chat-v1 | 38.12 | 59.33 | 40.45 | 74.46 | 72.41 | 73.76 | 50.94 | 58.49 |
+| AceGPT-v1.5-7B-Chat | 45.77 | 56.62 | 43.69 | 69.46 | 70.86 | 72.45 | 60.49 | 59.90 |
+| ChatGPT 3.5 Turbo | 46.07 | 57.72 | 45.63 | 74.45 | 76.88 | 76.12 | 60.24 | 62.44 |
+| AceGPT-v1.5-13B-Chat | 47.33 | 61.70 | 48.37 | 76.90 | 76.37 | 69.33 | 63.99 | 63.42 |
+| AceGPT-v2-8B-Chat | | |||||||
+| AceGPT-v2-32B-Chat | | |||||||
+| AceGPT-v2-70B-Chat | | |||||||
+| GPT-4 | 67.94 | 72.5 |||||||
+
+
+
+## Samples
+#### Sample1(abstract_algebra)
+
+#### Sample2(business_ethics)
+
+
+
+# Reference
+```
+@article{liang2024alignment,
+  title={Alignment at Pre-training! Towards Native Alignment for Arabic LLMs},
+  author={Liang, Juhao and Cai, Zhenyang and Zhu, Jianqing and Huang, Huang and Zong, Kewei and An, Bang and Alharthi, Mosen and He, Juncai and Zhang, Lian and Li, Haizhou and Wang, Benyou and Xu, Jinchao},
+  journal={},
+  year={2024}
+}
+```