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README.md
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#### π Pinned
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* [2024.05.
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* [2024.05.23] π We've released a comprehensive comparison between Phi-3-vision-128k-instruct and MiniCPM-Llama3-V 2.5, including benchmarks evaluations, multilingual capabilities, and inference efficiency ππππ. Click [here](https://github.com/OpenBMB/MiniCPM-V/blob/main/docs/compare_with_phi-3_vision.md) to view more details.
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* [2024.05.23] π₯π₯π₯ MiniCPM-V tops GitHub Trending and HuggingFace Trending! Our demo, recommended by Hugging Face Gradioβs official account, is available [here](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). Come and try it out!
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journal={arXiv preprint arXiv:2403.11703},
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year={2024}
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}
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```
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#### π Pinned
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* [2024.05.28] πππ MiniCPM-Llama3-V 2.5 now fully supports its feature in [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) and [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5)! Please pull the latest code for llama.cpp & ollama. We also release GGUF in various sizes [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf/tree/main). FAQ list for Ollama usage is comming within a day. Please stay tuned!
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* [2024.05.28] π« We now support LoRA fine-tuning for MiniCPM-Llama3-V 2.5, using only 2 V100 GPUs! See more statistics [here](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#model-fine-tuning-memory-usage-statistics).
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* [2024.05.23] π We've released a comprehensive comparison between Phi-3-vision-128k-instruct and MiniCPM-Llama3-V 2.5, including benchmarks evaluations, multilingual capabilities, and inference efficiency ππππ. Click [here](https://github.com/OpenBMB/MiniCPM-V/blob/main/docs/compare_with_phi-3_vision.md) to view more details.
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* [2024.05.23] π₯π₯π₯ MiniCPM-V tops GitHub Trending and HuggingFace Trending! Our demo, recommended by Hugging Face Gradioβs official account, is available [here](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). Come and try it out!
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journal={arXiv preprint arXiv:2403.11703},
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year={2024}
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}
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@article{yu2024rlaifv,
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title={RLAIF-V: Aligning MLLMs through Open-Source AI Feedback for Super GPT-4V Trustworthiness},
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author={Yu, Tianyu and Zhang, Haoye and Yao, Yuan and Dang, Yunkai and Chen, Da and Lu, Xiaoman and Cui, Ganqu and He, Taiwen and Liu, Zhiyuan and Chua, Tat-Seng and Sun, Maosong},
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journal={arXiv preprint arXiv:2405.17220},
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year={2024},
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}
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```
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