update docs

Signed-off-by: tc-mb <caitianchi@modelbest.cn>
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tc-mb
2025-09-19 15:26:08 +08:00
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- English
- 中文
- 한국어
- 日本語
- Deutsch
- Français
- Português
- Español
- မြန်မာဘာသာ
- ไทย
- Tiếng Việt
- Türkçe
- ܣܘܪܝܝܐ
- العربية
- हिन्दी
- বাংলা
- नेपाली
- Türkmençe
- Тоҷикӣ
- Кыргызча
- Русский
- Українська
- Беларуская
- ქართული
- Azərbaycanca
- Հայերեն
- Polski
- Lietuvių
- Eesti
- Latviešu
- Čeština
- Slovenčina
- Magyar
- Slovenščina
- Hrvatski
- Bosanski
- Crnogorski
- Српски
- Shqip
- Română
- Български
- Македонски
## 支持语言
英语
中文
韩语
日语
德语
法语
葡萄牙语
西班牙语
缅甸语
泰语
越南语
土耳其语
叙利亚语
阿拉伯语
印地语
孟加拉语
尼泊尔语
土库曼语
塔吉克语
吉尔吉斯语
俄语
乌克兰语
白俄罗斯语
格鲁吉亚语
阿塞拜疆语
亚美尼亚语
波兰语
立陶宛语
爱沙尼亚语
拉脱维亚语
捷克语
斯洛伐克语
匈牙利语
斯洛文尼亚语
克罗地亚语
波斯尼亚语
黑山语
塞尔维亚语
阿尔巴尼亚语
罗马尼亚语
保加利亚
马其顿语
## Supported Languages
English
Chinese
Korean
Japanese
German
French
Portuguese
Spanish
Burmese
Thai
Vietnamese
Turkish
Syriac
Arabic
Hindi
Bengali
Nepali
Turkmen
Tajik
Kyrgyz
Russian
Ukrainian
Belarusian
Georgian
Azerbaijani
Armenian
Polish
Lithuanian
Estonian
Latvian
Czech
Slovak
Hungarian
Slovenian
Croatian
Bosnian
Montenegrin
Serbian
Albanian
Romanian
Bulgarian
Macedonian

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Leveraging the latest [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) method (the newest technique in the [RLHF-V](https://github.com/RLHF-V) [CVPR'24] series), MiniCPM-Llama3-V 2.5 exhibits more trustworthy behavior. It achieves a **10.3%** hallucination rate on Object HalBench, lower than GPT-4V-1106 (13.6%), achieving the best-level performance within the open-source community. [Data released](https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset). Leveraging the latest [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) method (the newest technique in the [RLHF-V](https://github.com/RLHF-V) [CVPR'24] series), MiniCPM-Llama3-V 2.5 exhibits more trustworthy behavior. It achieves a **10.3%** hallucination rate on Object HalBench, lower than GPT-4V-1106 (13.6%), achieving the best-level performance within the open-source community. [Data released](https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset).
- 🌏 **Multilingual Support.** - 🌏 **Multilingual Support.**
Thanks to the strong multilingual capabilities of Llama 3 and the cross-lingual generalization technique from [VisCPM](https://github.com/OpenBMB/VisCPM), MiniCPM-Llama3-V 2.5 extends its bilingual (Chinese-English) multimodal capabilities to **over 30 languages including German, French, Spanish, Italian, Korean etc.** [All Supported Languages](./assets/minicpm-llama-v-2-5_languages.md). Thanks to the strong multilingual capabilities of Llama 3 and the cross-lingual generalization technique from [VisCPM](https://github.com/OpenBMB/VisCPM), MiniCPM-Llama3-V 2.5 extends its bilingual (Chinese-English) multimodal capabilities to **over 30 languages including German, French, Spanish, Italian, Korean etc.** [All Supported Languages](../docs/minicpm-llama-v-2-5_languages.md).
- 🚀 **Efficient Deployment.** - 🚀 **Efficient Deployment.**
MiniCPM-Llama3-V 2.5 systematically employs **model quantization, CPU optimizations, NPU optimizations and compilation optimizations**, achieving high-efficiency deployment on end-side devices. For mobile phones with Qualcomm chips, we have integrated the NPU acceleration framework QNN into llama.cpp for the first time. After systematic optimization, MiniCPM-Llama3-V 2.5 has realized a **150x acceleration in end-side MLLM image encoding** and a **3x speedup in language decoding**. MiniCPM-Llama3-V 2.5 systematically employs **model quantization, CPU optimizations, NPU optimizations and compilation optimizations**, achieving high-efficiency deployment on end-side devices. For mobile phones with Qualcomm chips, we have integrated the NPU acceleration framework QNN into llama.cpp for the first time. After systematic optimization, MiniCPM-Llama3-V 2.5 has realized a **150x acceleration in end-side MLLM image encoding** and a **3x speedup in language decoding**.