Update model card: remove transformers tag, add paper/project links

README.md

@@ -2,7 +2,6 @@
 language:
 - zh
 - en
-library_name: transformers
 license: apache-2.0
 pipeline_tag: text-generation
 ---
@@ -13,14 +12,15 @@ pipeline_tag: text-generation
 
 <p align="center">
 <a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">GitHub Repo</a> |
-<a href="https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf" target="_blank">Technical Report</a> 
+<a href="https://huggingface.co/papers/2506.07900" target="_blank">Paper: MiniCPM4: Ultra-Efficient LLMs on End Devices</a> |
+<a href="https://huggingface.co/collections/openbmb/minicpm4-6841ab29d180257e940baa9b" target="_blank">Hugging Face Collection</a>
 </p>
 <p align="center">
 👋 Join us on <a href="https://discord.gg/3cGQn9b3YM" target="_blank">Discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a>
 </p>
 
 ## What's New
-- [2025.06.06] **MiniCPM4** series are released! This model achieves ultimate efficiency improvements while maintaining optimal performance at the same scale! It can achieve over 5x generation acceleration on typical end-side chips! You can find technical report [here](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf).🔥🔥🔥
+- [2025.06.06] **MiniCPM4** series are released! This model achieves ultimate efficiency improvements while maintaining optimal performance at the same scale! It can achieve over 5x generation acceleration on typical end-side chips! You can find technical report [here](https://huggingface.co/papers/2506.07900).🔥🔥🔥
 
 ## MiniCPM4 Series
 MiniCPM4 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems.
@@ -60,7 +60,10 @@ MiniCPM 4 is an extremely efficient edge-side large model that has undergone eff
 ### Inference with [llama.cpp](https://github.com/ggml-org/llama.cpp)
 
 ```bash
-./llama-cli -c 1024 -m MiniCPM4-8B-Q4_K_M.gguf -n 1024 --top-p 0.7 --temp 0.7 --prompt "<|im_start|>user\n请写一篇关于人工智能的文章，详细介绍人工智能的未来发展和隐患。<|im_end|>\n<|im_start|>assistant\n"
+./llama-cli -c 1024 -m MiniCPM4-8B-Q4_K_M.gguf -n 1024 --top-p 0.7 --temp 0.7 --prompt "<|im_start|>user
+请写一篇关于人工智能的文章，详细介绍人工智能的未来发展和隐患。<|im_end|>
+<|im_start|>assistant
+"
 ```
 
 ## Statement
@@ -73,7 +76,7 @@ MiniCPM 4 is an extremely efficient edge-side large model that has undergone eff
 - This repository and MiniCPM models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. 
 
 ## Citation
-- Please cite our [paper](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf) if you find our work valuable.
+- Please cite our [paper](https://huggingface.co/papers/2506.07900) if you find our work valuable.
 
 ```bibtex
 @article{minicpm4,