FreedomIntelligence/AceGPT-v2-8B-Chat

AceGPT

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 8B-chat pre-trained model, developed based on AceGPT-v2-8B.

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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) and the Shenzhen Research Institute of Big Data (SRIBD).

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.

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 is assessed based on its source settings.)

	MMLU (Huang et al. (2023))	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
Qwen1.5-32B-Chat	51.99	57.35	46.29	78.08	78.26	77.61	71.25	65.83
AceGPT-v2-8B-Chat	54.45	62.21	52.98	76.54	76.55	71.65	72.44	66.69
AceGPT-v2-32B-Chat	57.12	68.70	52.89	81.36	79.03	77.22	78.07	70.63
AceGPT-v2-70B-Chat	64.26	72.50	56.99	78.61	77.38	82.66	85.53	73.99
GPT-4	65.04	72.50	57.76	84.06	79.43	85.99	85.67	75.78

Samples

Sample1(abstract_algebra)

• input: "<User>: فيما يلي أسئلة الاختيار من متعدد حول جبر تجريدي\n\nسؤال: ما هو الدرجة للامتداد الميداني الناتج من Q(sqrt(2), sqrt(3), sqrt(18)) على Q؟\nA. 0\nB. 4\nC. 2\nD. 6\nمن فضلك اختر إجابة واحدة من بين 'A، B، C، D' دون شرح. <Assistant>: "

• output: "B\n\nالشرح:\n\nالإجابة هي"

Sample2(business_ethics)

• input: "<User>: فيما يلي أسئلة الاختيار من متعدد حول أخلاقيات الأعمال\n\nسؤال: تُصبح _______ مثل البيتكوين أكثر انتشارًا وتحمل مجموعة كبيرة من الآثار الأخلاقية المرتبطة بها، على سبيل المثال، إنها _______ وأكثر _______. ومع ذلك، تم استخدامها أيضًا للمشاركة في _______.\nA. العملات الرقمية، مكلفة، آمنة، جرائم مالية\nB. العملات التقليدية، رخيصة، غير آمنة، العطاء الخيري\nC. العملات الرقمية، رخيصة، آمنة، جرائم مالية\nD. العملات التقليدية، مكلفة، غير آمنة، العطاء الخيري\nمن فضلك اختر إجابة واحدة من بين 'A، B، C، D' دون شرح. <Assistant>: "

• output: "C\n\nالشرح:\n\nالإجابة هي"

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}
}

@article{zhu2024second,
  title={Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion},
  author={Zhu, Jianqing and Huang, Huang and Lin, Zhihang and Liang, Juhao and Tang, Zhengyang and Almubarak, Khalid and Alharthi, Mosen and An, Bang and He, Juncai and Wu, Xiangbo and Yu, Fei and Chen, Junying and Ma, Zhuoheng and Du, Yuhao and Hu, Yan and Zhang, He and Alghamdi, Emad A. and Zhang, Lian and Sun, Ruoyu and Li, Haizhou and Wang, Benyou and Xu, Jinchao},
  journal={},
  year={2024}
}