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docs/minicpm-llama-v-2-5_languages.md
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- English
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- 中文
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- 한국어
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- 日本語
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- Deutsch
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- Français
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- Português
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- Español
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- မြန်မာဘာသာ
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- ไทย
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- Tiếng Việt
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- Türkçe
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- ܣܘܪܝܝܐ
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- العربية
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- हिन्दी
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- বাংলা
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- नेपाली
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- Türkmençe
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- Тоҷикӣ
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- Кыргызча
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- Русский
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- Українська
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- Беларуская
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- ქართული
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- Azərbaycanca
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- Հայերեն
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- Polski
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- Lietuvių
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- Eesti
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- Latviešu
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- Čeština
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- Slovenčina
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- Magyar
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- Slovenščina
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- Hrvatski
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- Bosanski
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- Crnogorski
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- Српски
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- Shqip
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- Română
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- Български
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- Македонски
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## 支持语言
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英语
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中文
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韩语
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日语
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德语
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法语
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葡萄牙语
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西班牙语
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缅甸语
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泰语
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越南语
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土耳其语
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叙利亚语
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阿拉伯语
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印地语
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孟加拉语
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尼泊尔语
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土库曼语
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塔吉克语
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吉尔吉斯语
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俄语
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乌克兰语
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白俄罗斯语
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格鲁吉亚语
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阿塞拜疆语
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亚美尼亚语
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波兰语
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立陶宛语
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爱沙尼亚语
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拉脱维亚语
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捷克语
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斯洛伐克语
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匈牙利语
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斯洛文尼亚语
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克罗地亚语
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波斯尼亚语
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黑山语
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塞尔维亚语
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阿尔巴尼亚语
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罗马尼亚语
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保加利亚
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马其顿语
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## Supported Languages
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English
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Chinese
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Korean
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Japanese
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German
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French
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Portuguese
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Spanish
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Burmese
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Thai
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Vietnamese
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Turkish
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Syriac
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Arabic
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Hindi
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Bengali
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Nepali
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Turkmen
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Tajik
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Kyrgyz
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Russian
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Ukrainian
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Belarusian
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Georgian
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Azerbaijani
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Armenian
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Polish
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Lithuanian
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Estonian
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Latvian
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Czech
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Slovak
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Hungarian
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Slovenian
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Croatian
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Bosnian
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Montenegrin
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Serbian
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Albanian
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Romanian
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Bulgarian
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Macedonian
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@@ -15,7 +15,7 @@
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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).
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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).
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- 🌏 **Multilingual Support.**
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- 🌏 **Multilingual Support.**
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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).
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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).
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- 🚀 **Efficient Deployment.**
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- 🚀 **Efficient Deployment.**
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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**.
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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**.
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