Model reference · open weights

AceGPT

LLMs FreedomIntelligence Text gen 1 build Open weights 1k dl/mo

AceGPT is an open-weight language model from FreedomIntelligence. AceGPT-v2-8B-Chat (FP16) weighs 16.1 GB; the smallest configuration that runs it is 2× RTX 3060 12 GB.

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  • AceGPT-v2-8B-Chat is an 8.0B parameter text-generation model developed by FreedomIntelligence, specifically optimized for dialogue applications with a focus on the Arabic language domain.
  • The model supports Arabic, Chinese, and English, operates with an 8192 token context length, and is distributed under the Apache-2.0 license.

Summary of the FreedomIntelligence/AceGPT-v2-8B-Chat model card, 2026-10-01

What it is

Released byFreedomIntelligence
Released2024-06-20
Parameters8.0B
VRAM16.1 GB for the weights

What it runs on

Memory and cards for AceGPT-v2-8B-Chat (FP16)

16.1 GBweights, file size
131 MBcache per 1K tokens
745 MBruntime overhead, at least
8,192 tokenscontext max
CardRequests at onceContext maxMemory
8K each
RTX 3060 12 GB … RTX 4060 Ti 16 GB
2 smaller cards
RTX 3090 24 GB6all 8K23.4 GB
RTX 4090 24 GB6all 8K23.4 GB
RTX 5090 32 GB13all 8K31.0 GB
L40S 48 GB25all 8K44.0 GB
A100 80 GB57all 8K78.2 GB
H100 80 GB53all 8K78.1 GB
RTX PRO 6000 Blackwell 96 GB67all 8K93.8 GB
DGX Spark (GB10) 128 GB unified80all 8K107 GB
H200 141 GB109all 8K138 GB
B200 180 GB143all 8K176 GB
2× RTX 3060 12 GB
tensor parallel
5all 8K11.6 GB a card
2× RTX 4060 Ti 16 GB
tensor parallel
12all 8K15.4 GB a card
2× RTX 4090 24 GB
tensor parallel
27all 8K23.4 GB a card
2× RTX 3090 24 GB
tensor parallel
27all 8K23.4 GB a card
Memory needed at each load
Requests at once8K tokens each
117.9 GB
522.2 GB
825.4 GB
1634.0 GB
3251.2 GB
6485.5 GB

One card, with vLLM's small-card settings.

From the model card

What FreedomIntelligence says about AceGPT

Read the model card

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.


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))ArabicMMLUEXAMSACVA (clean)ACVA (all)Arabic BoolQArabic ARC-CAverage
LLaMA2-7B-chat13.7833.4013.0520.9921.8034.9223.7221.09
Llama2-13B-chat8.9236.1216.1135.1235.7154.1327.4730.51
Jais-13B-chat19.5254.8319.7166.7561.4141.2511.9539.34
Phoenix-7b29.7244.7431.9343.8041.8666.7033.5341.75
AceGPT-7B-chat30.6936.3133.7353.8753.0760.7038.0543.77
Mistral-7B-Instruct-v0.227.9341.4421.5664.5663.4760.1835.6744.97
AceGPT-13B-Chat35.5952.6138.7270.8270.2166.8544.2054.14
Jais-30B-chat-v335.6862.3632.2473.6373.6676.3051.0257.84
Jais-30B-chat-v138.1259.3340.4574.4672.4173.7650.9458.49
AceGPT-v1.5-7B-Chat45.7756.6243.6969.4670.8672.4560.4959.90
ChatGPT 3.5 Turbo46.0757.7245.6374.4576.8876.1260.2462.44
AceGPT-v1.5-13B-Chat47.3361.7048.3776.9076.3769.3363.9963.42
Qwen1.5-32B-Chat51.9957.3546.2978.0878.2677.6171.2565.83
AceGPT-v2-8B-Chat54.4562.2152.9876.5476.5571.6572.4466.69
AceGPT-v2-32B-Chat57.1268.7052.8981.3679.0377.2278.0770.63
AceGPT-v2-70B-Chat64.2672.5056.9978.6177.3882.6685.5373.99
GPT-465.0472.5057.7684.0679.4385.9985.6775.78

Samples

Sample1(abstract_algebra)
  • input: ": 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. : "

  • output: "B\n\nالشرح:\n\nالإجابة هي"

Sample2(business_ethics)
  • input: ": 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. : "

  • output: "C\n\nالشرح:\n\nالإجابة هي"

Reference

@inproceedings{liang2024alignment,
  title={Alignment at Pre-training! Towards Native Alignment for Arabic {LLM}s},
  author={Juhao Liang and Zhenyang Cai and Jianqing Zhu and Huang Huang and Kewei Zong and Bang An and Mosen Alharthi and Juncai He and Lian Zhang and Haizhou Li and Benyou Wang and Jinchao Xu},
  booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
  year={2024},
  url={https://openreview.net/forum?id=woRFmNJiLp}
}
@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}
}

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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