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README.md

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-license: other
-license_name: openbmb-general-model-license
-license_link: >-
-  https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md
----
+---
+license: other
+license_name: openbmb-general-model-license
+license_link: >-
+  https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md
+---
+
+![](https://aiot.aidlux.com/_next/image?url=%2Fapi%2Fv1%2Ffiles%2Fmodel%2Fcover%2F20250108152410_minicpm_logo.png&w=640&q=75)
+
+## MiniCPM-1B-sft
+
+MiniCPM is an End-Size LLM developed by ModelBest Inc. and TsinghuaNLP, with only 1.2B parameters excluding embeddings.
+
+## Model Details
+
+- MiniCPM has very close performance compared with Mistral-7B on open-sourced general benchmarks with better ability on Chinese, Mathmetics and Coding after SFT. The overall performance exceeds Llama2-13B, MPT-30B, Falcon-40B, etc.
+- After DPO, MiniCPM outperforms Llama2-70B-Chat, Vicuna-33B, Mistral-7B-Instruct-v0.1, Zephyr-7B-alpha, etc. on MTBench.
+- MiniCPM-V, based on MiniCPM-2B, achieves the best overall performance among multimodel models of the same scale, surpassing existing multimodal large models built on Phi-2 and achieving performance comparable to or even better than 9.6B Qwen-VL-Chat on some tasks.
+- MiniCPM can be deployed and infer on smartphones, and the speed of streaming output is relatively higher than the verbal speed of human. MiniCPM-V is the first multi-modal models that can be deployed on smartphones.
+- The cost of developing based on MiniCPM is low. Parameter efficient finetuning can be conducted with a single 1080/2080 GPU and full parameter finetuning can be conducted with a 3090/4090 GPU.
+
+## Source Model Evaluation
+
+> Note: This table showed source model instead of quantized model evaluation. Source Model Evaluation refer to [MiniCPM-1B-sft Evaluation Results](https://github.com/OpenBMB/MiniCPM?tab=readme-ov-file#minicpm-s-1b-%E8%AF%84%E6%B5%8B%E7%BB%93%E6%9E%9C)
+
+- Code Generation：Average pass@1 score of HumanEval(0-shot) and MBPP(3-shot).
+- Commonsense Reasoning: Average 0-shot accuracy of PIQA, SIQA, HellaSwag, WinoGrande and COPA.
+- Reading Comprehension: Average 0-shot accuracy of BoolQ, LAMBADA and TyDi-QA.
+- Other Benchmarks: average performance of GSM8K(8-shot)、MMLU(5-shot)、BBH(3-shot) and AGI-Eval(0-shot).
+
+|        Setting        | Average<br>Sparsity | Average<br>Performance | Code<br>Generation | Commonsense<br>Reasoning | Reading<br>Comprehension | GSM8K | MMLU  |  BBH  | AGI-Eval |
+| :-------------------: | :----------------: | :----------------------: | :----------------------: | :---: | :---: | :---: | :---------: | :-----: | :-----------------: |
+| LLaMA2-7B    | - | 37.96 | 16.37 | 69.59 | 61.87 | 12.96 | 44.45 | 32.96 | 27.53 |
+| ReluLLaMA-7B | 66.98 | 37.62 | 15.85 | 69.64 | 70.54 |  5.84 | 38.64 | 35.07 | 27.73 |
+| **ProSparse-7B**\* | 88.11 | 38.31 | 19.47 | 66.29 | 63.33 | 12.74 | 45.21 | 33.59 | 27.55 |
+| **ProSparse-7B**   | **89.32** | **38.46** | 19.42 | 66.27 | 63.50 | 12.13 | 45.48 | 34.99 | 27.46 |
+| LLaMA2-13B | - | 44.06 | 20.19 | 72.58 | 71.55 | 22.21 | 54.69 | 37.89 | 29.33 |
+| ReluLLaMA-13B | 71.56 | 42.74 | 20.19 | 70.44 | 73.29 | 18.50 | 50.58 | 37.97 | 28.22 |
+| **ProSparse-13B**\* | 87.97 | **45.07** | 29.03 | 69.75 | 67.54 | 25.40 | 54.78 | 40.20 | 28.76 |
+| **ProSparse-13B**   | **88.80** | 44.90 | 28.42 | 69.76 | 66.91 | 26.31 | 54.35 | 39.90 | 28.67 |
+| MiniCPM-1B | - | 44.44 | 36.85 | 63.67 | 60.90 | 35.48 | 50.44 | 35.03 | 28.71 |
+| **MiniCPM-S-1B**\*  | 86.25 | **44.72** | 41.38 | 64.55 | 60.69 | 34.72 | 49.36 | 34.04 | 28.27 |
+| **MiniCPM-S-1B**    | **87.89** | **44.72** | 42.04 | 64.37 | 60.73 | 34.57 | 49.51 | 34.08 | 27.77 |
+
+## Performance Reference
+
+Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
+
+## Inference & Model Conversion
+
+Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
+
+## License
+
+- Source Model: [OpenBMB-General-Model-License](https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md)
+
+- Deployable Model: [OpenBMB-General-Model-License](https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md)