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@@ -109,7 +109,7 @@ Furthermore, across specific benchmarks—including test case generation, code p
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  | SWE-Review | 8.9 | 3.4 | 10.5 | 16.2 | x | x | 6.4 |
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  | OctoCodingbench | 26.1 | 13.3 | 22.8 | 36.2 | 22.9 | x | 26.0 |
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- To evaluate the model's full-stack capability to architect complete, functional applications "from zero to one," we established a novel benchmark: VIBE (Visual & Interactive Benchmark for Execution). This suite encompasses five core subsets: Web, Simulation, Android, iOS, and Backend. Distinguishing itself from traditional benchmarks, VIBE leverages an innovative Agent-as-a-Verifier (AaaV) paradigm to automatically assess the interactive logic and visual aesthetics of generated applications within a real runtime environment.
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  MiniMax-M2.1 delivers outstanding performance on the VIBE aggregate benchmark, achieving an average score of 88.6—demonstrating robust full-stack development capabilities. It excels particularly in the VIBE-Web (91.5) and VIBE-Android (89.7) subsets.
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@@ -169,6 +169,10 @@ We recommend using [vLLM](https://github.com/vllm-project/vllm) to serve MiniMax
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  We recommend using [Transformers](https://github.com/huggingface/transformers) to serve MiniMax-M2.1. Please refer to our [Transformers Deployment Guide](./docs/transformers_deploy_guide.md).
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  ### Inference Parameters
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  We recommend using the following parameters for best performance: `temperature=1.0`, `top_p = 0.95`, `top_k = 40`. Default system prompt:
 
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  | SWE-Review | 8.9 | 3.4 | 10.5 | 16.2 | x | x | 6.4 |
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  | OctoCodingbench | 26.1 | 13.3 | 22.8 | 36.2 | 22.9 | x | 26.0 |
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+ To evaluate the model's full-stack capability to architect complete, functional applications "from zero to one," we established a novel benchmark: [VIBE (Visual & Interactive Benchmark for Execution)](https://huggingface.co/datasets/MiniMaxAI/VIBE). This suite encompasses five core subsets: Web, Simulation, Android, iOS, and Backend. Distinguishing itself from traditional benchmarks, VIBE leverages an innovative Agent-as-a-Verifier (AaaV) paradigm to automatically assess the interactive logic and visual aesthetics of generated applications within a real runtime environment.
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  MiniMax-M2.1 delivers outstanding performance on the VIBE aggregate benchmark, achieving an average score of 88.6—demonstrating robust full-stack development capabilities. It excels particularly in the VIBE-Web (91.5) and VIBE-Android (89.7) subsets.
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  We recommend using [Transformers](https://github.com/huggingface/transformers) to serve MiniMax-M2.1. Please refer to our [Transformers Deployment Guide](./docs/transformers_deploy_guide.md).
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+ ### Other Inference Engines
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+ - [KTransformers](https://github.com/kvcache-ai/ktransformers)
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  ### Inference Parameters
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  We recommend using the following parameters for best performance: `temperature=1.0`, `top_p = 0.95`, `top_k = 40`. Default system prompt: