Add library name, link to paper and Github repository

README.md

@@ -1,6 +1,7 @@
 ---
-pipeline_tag: text-generation
 license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
 ---
 
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-  <a href="https://arxiv.org/abs/2506.13585" target="_blank" style="margin: 2px;">
-    <img alt="Paper" src="https://img.shields.io/badge/📖_Paper-MiniMax--M1-FF4040?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;"/>
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-  </a>
-  <a href="https://github.com/MiniMax-AI/MiniMax-MCP" style="margin: 2px;">
-    <img alt="MCP" src="https://img.shields.io/badge/🚀_MCP-MiniMax_MCP-FF4040?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;"/>
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-</div>
-<div align="center" style="line-height: 1;">
-  <a href="https://huggingface.co/MiniMaxAI" target="_blank" style="margin: 2px;">
-    <img alt="Hugging Face" src="https://img.shields.io/badge/🤗_Hugging_Face-MiniMax-FF4040?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;"/>
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-
-# MiniMax-M1
-
-## 1. Model Overview 
-
-We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.
-MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combined with a lightning
-attention mechanism. The model is developed based on our previous [MiniMax-Text-01 model](https://huggingface.co/MiniMaxAI/MiniMax-Text-01), 
-which contains a total of 456 billion parameters with 45.9 billion parameters activated
-per token. Consistent with MiniMax-Text-01, the M1 model natively supports a context length of 1
-million tokens, 8x the context size of DeepSeek R1. Furthermore, the lightning attention mechanism
-in MiniMax-M1 enables efficient scaling of test-time compute – For example, compared to DeepSeek
-R1, M1 consumes 25% of the FLOPs at a generation length of 100K tokens. These properties make M1
-particularly suitable for complex tasks that require processing long inputs and thinking extensively.
-MiniMax-M1 is trained using large-scale reinforcement learning (RL) on diverse problems ranging from
-traditional mathematical reasoning to sandbox-based, real-world software engineering environments.
-We develop an efficient RL scaling framework for M1 highlighting two perspectives: (1) We propose
-CISPO, a novel algorithm that clips importance sampling weights instead of token updates, which
-outperforms other competitive RL variants; (2) Our hybrid-attention design naturally enhances the
-efficiency of RL, where we address unique challenges when scaling RL with the hybrid architecture. We
-train two versions of MiniMax-M1 models with [40K](https://huggingface.co/MiniMaxAI/MiniMax-M1-40k) and 
-[80K](https://huggingface.co/MiniMaxAI/MiniMax-M1-80k) thinking budgets respectively. Experiments
-on standard benchmarks show that our models outperform other strong open-weight models such as
-the original DeepSeek-R1 and Qwen3-235B, particularly on complex software engineering, tool using,
-and long context tasks. With efficient scaling of test-time compute, MiniMax-M1 serves as a strong
-foundation for next-generation language model agents to reason and tackle real-world challenges. 
-
-<p align="center">
-  <img width="100%" src="figures/TextBench.png">
-  <br>
-  <small><em>Benchmark performance comparison of leading commercial and open-weight models across competition-level mathematics, coding, software engineering, agentic tool use, and long-context understanding tasks. We use the MiniMax-M1-80k model here for MiniMax-M1.</em></small>
-</p>
-
-
-## 2. Evaluation
-
-**Performance of MiniMax-M1 on core benchmarks.**
-
-
-| **Category** | **Task** | **MiniMax-M1-80K** | **MiniMax-M1-40K** | **Qwen3-235B-A22B** | **DeepSeek-R1-0528** | **DeepSeek-R1** | **Seed-Thinking-v1.5** | **Claude 4 Opus** | **Gemini 2.5 Pro (06-05)** | **OpenAI-o3** |
-|:---|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
-| | *Extended Thinking* | *80K* | *40K* | *32k* | *64k* | *32k* | *32k* | *64k* | *64k* | *100k* |
-| ***Mathematics*** | AIME 2024 | 86.0 | 83.3 | 85.7 | 91.4 | 79.8 | 86.7 | 76.0 | 92.0 | 91.6 |
-| | AIME 2025 | 76.9 | 74.6 | 81.5 | 87.5 | 70.0 | 74.0 | 75.5 | 88.0 | 88.9 |
-| | MATH-500 | 96.8 | 96.0 | 96.2 | 98.0 | 97.3 | 96.7 | 98.2 | 98.8 | 98.1 |
-| ***General Coding*** | LiveCodeBench *(24/8~25/5)* | 65.0 | 62.3 | 65.9 | 73.1 | 55.9 | 67.5 | 56.6 | 77.1 | 75.8 |
-| | FullStackBench | 68.3 | 67.6 | 62.9 | 69.4 | 70.1 | 69.9 | 70.3 | -- | 69.3 |
-| ***Reasoning & Knowledge***| GPQA Diamond | 70.0 | 69.2 | 71.1 | 81.0 | 71.5 | 77.3 | 79.6 | 86.4 | 83.3 |
-| | HLE *(no tools)* | 8.4\* | 7.2\* | 7.6\* | 17.7\* | 8.6\* | 8.2 | 10.7 | 21.6 | 20.3 |
-| | ZebraLogic | 86.8 | 80.1 | 80.3 | 95.1 | 78.7 | 84.4 | 95.1 | 91.6 | 95.8 |
-| | MMLU-Pro | 81.1 | 80.6 | 83.0 | 85.0 | 84.0 | 87.0 | 85.0 | 86.0 | 85.0 |
-| ***Software Engineering***| SWE-bench Verified| 56.0 | 55.6 | 34.4 | 57.6 | 49.2 | 47.0 | 72.5 | 67.2 | 69.1 |
-| ***Long Context*** | OpenAI-MRCR *(128k)* | 73.4 | 76.1 | 27.7 | 51.5 | 35.8 | 54.3 | 48.9 | 76.8 | 56.5 |
-| | OpenAI-MRCR *(1M)* | 56.2 | 58.6 | -- | -- | -- | -- | -- | 58.8 | -- |
-| | LongBench-v2 | 61.5 | 61.0 | 50.1 | 52.1 | 58.3 | 52.5 | 55.6 | 65.0 | 58.8 |
-| ***Agentic Tool Use***| TAU-bench *(airline)* | 62.0 | 60.0 | 34.7 | 53.5 | -- | 44.0 | 59.6 | 50.0 | 52.0 |
-| | TAU-bench *(retail)* | 63.5 | 67.8 | 58.6 | 63.9 | -- | 55.7 | 81.4 | 67.0 | 73.9 |
-| ***Factuality*** | SimpleQA | 18.5 | 17.9 | 11.0 | 27.8 | 30.1 | 12.9 | -- | 54.0 | 49.4 |
-| ***General Assistant***| MultiChallenge | 44.7 | 44.7 | 40.0 | 45.0 | 40.7 | 43.0 | 45.8 | 51.8 | 56.5 |
-
-\* conducted on the text-only HLE subset.
-
-Our models are evaluated with `temperature=1.0`, `top_p=0.95`. 
-
-### SWE-bench methodology 
-We report results derived from the Agentless scaffold. Departing from the original pipeline, our methodology employs a two-stage localization process (without any embedding-based retrieval mechanisms): initial coarse-grained file localization followed by fine-grained localization to specific files and code elements. The values for our models are calculated on the subset of n=486 verified tasks which work on our infrastructure. The excluded 14 test cases that were incompatible with our internal infrastructure are:
-`"astropy__astropy-7606"`,
-`"astropy__astropy-8707"`,
-`"astropy__astropy-8872"`,
-`"django__django-10097"`,
-`"matplotlib__matplotlib-20488"`,
-`"psf__requests-2317"`,
-`"psf__requests-2931"`,
-`"psf__requests-5414"`,
-`"pylint-dev__pylint-6528"`,
-`"pylint-dev__pylint-7277"`,
-`"sphinx-doc__sphinx-10435"`,
-`"sphinx-doc__sphinx-7985"`,
-`"sphinx-doc__sphinx-8269"`,
-`"sphinx-doc__sphinx-8475"`
-
-### TAU-bench methodology 
-We evaluate TAU-Bench with GPT-4.1 as user model and without any custom tools. The maximum number of interaction steps is 40. 
-Our general system prompt is: 
-```
-- In each round, you need to carefully examine the tools provided to you to determine if any can be used.
-- You must adhere to all of the policies. Pay attention to the details in the terms. Solutions for most situations can be found within these policies.
-``` 
-
-## 3. Deployment Guide
-
-Download the model from HuggingFace repository: 
-- [MiniMax-M1-40k](https://huggingface.co/MiniMaxAI/MiniMax-M1-40k)
-- [MiniMax-M1-80k](https://huggingface.co/MiniMaxAI/MiniMax-M1-80k)
-
-For production deployment, we recommend using [vLLM](https://docs.vllm.ai/en/latest/) to serve MiniMax-M1. vLLM provides excellent performance for serving large language models with the following features:
-- 🔥 Outstanding service throughout performance
-- ⚡ Efficient and intelligent memory management
-- 📦 Powerful batch request processing capability
-- ⚙️ Deeply optimized underlying performance
-
-For detailed vLLM deployment instructions, please refer to our [vLLM Deployment Guide](./docs/vllm_deployment_guide.md).
-Alternatively, you can also deploy using Transformers directly. For detailed Transformers deployment instructions, you can see our [MiniMax-M1 Transformers Deployment Guide](./docs/transformers_deployment_guide.md).
-
-
-## 4. Function Calling
-
-The MiniMax-M1 model supports function calling capabilities, enabling the model to identify when external functions need to be called and output function call parameters in a structured format. [MiniMax-M1 Function Call Guide](./docs/function_call_guide.md) provides detailed instructions on how to use the function calling feature of MiniMax-M1.
-
-
-## 5. Chatbot & API
-For general use and evaluation, we provide a [Chatbot](https://chat.minimax.io/) with online search capabilities and the [online API](https://www.minimax.io/platform/) for developers. For general use and evaluation, we provide the [MiniMax MCP Server](https://github.com/MiniMax-AI/MiniMax-MCP) with video generation, image generation, speech synthesis, and voice cloning for developers.
-
-
-## 6. Contact Us
-Contact us at [model@minimax.io](mailto:model@minimax.io).
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