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README.md +220 -223
docs/function_call_guide.md +64 -218
docs/function_call_guide_cn.md +61 -216
docs/vllm_deployment_guide.md +22 -12
docs/vllm_deployment_guide_cn.md +19 -11
figures/wechat-qrcode.jpeg +0 -0

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----
-pipeline_tag: text-generation
-license: apache-2.0
-library_name: transformers
-tags:
-- vllm
----
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-<hr>
-<div align="center" style="line-height: 1;">
-  <a href="https://www.minimax.io" target="_blank" style="margin: 2px;">
-    <img alt="Homepage" src="https://img.shields.io/badge/_Homepage-MiniMax-FF4040?style=flat-square&labelColor=2C3E50&logo=data:image/svg+xml;base64,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&logoWidth=20" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <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;"/>
-  </a>
-  <a href="https://chat.minimax.io/" target="_blank" style="margin: 2px;">
-    <img alt="Chat" src="https://img.shields.io/badge/_MiniMax_Chat-FF4040?style=flat-square&labelColor=2C3E50&logo=data:image/svg+xml;base64,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&logoWidth=20" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://www.minimax.io/platform" style="margin: 2px;">
-    <img alt="API" src="https://img.shields.io/badge/⚡_API-Platform-FF4040?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;"/>
-  </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;"/>
-  </a>
-</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;"/>
-  </a>
-  <a href="https://github.com/MiniMax-AI/MiniMax-M1" target="_blank" style="margin: 2px;">
-    <img alt="GitHub" src="https://img.shields.io/badge/🐙_GitHub-MiniMax-FF4040?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://www.modelscope.cn/organization/MiniMax" target="_blank" style="margin: 2px;">
-    <img alt="ModelScope" src="https://img.shields.io/badge/🤖️_ModelScope-MiniMax-FF4040?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://github.com/MiniMax-AI/MiniMax-M1/blob/main/LICENSE" style="margin: 2px;">
-    <img alt="License" src="https://img.shields.io/badge/⚖️_License-Apache_2.0-FF4040?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://github.com/MiniMax-AI/MiniMax-AI.github.io/blob/main/images/wechat-qrcode.jpeg" target="_blank" style="margin: 2px;">
-    <img alt="WeChat" src="https://img.shields.io/badge/💬_WeChat-MiniMax-FF4040?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
-# 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. Recommendations for Minimax-M1 Model Usage
-To achieve the best results with the Minimax-M1 model, we suggest focusing on two key points: Inference Parameters and the System Prompt.
-### 3.1. Inference Parameters
-- Temperature: **`1.0`**
-- Top_p: **`0.95`**
-This setting is optimal for encouraging creativity and diversity in the model's responses. It allows the model to explore a wider range of linguistic possibilities, preventing outputs that are too rigid or repetitive, while still maintaining strong logical coherence.
-### 3.2. System Prompt
-Tailoring your system prompt to the specific task is crucial for guiding the model effectively. Below are suggested settings for different scenarios.
-#### A. General-Purpose Scenarios
-For common tasks like summarization, translation, Q&A, or creative writing:
-```
-You are a helpful assistant.
-```
-#### B. Web Development Scenarios
-For complex tasks like generating code for web pages:
-```
-You are a web development engineer, writing web pages according to the instructions below. You are a powerful code editing assistant capable of writing code and creating artifacts in conversations with users, or modifying and updating existing artifacts as requested by users.
-All code is written in a single code block to form a complete code file for display, without separating HTML and JavaScript code. An artifact refers to a runnable complete code snippet, you prefer to integrate and output such complete runnable code rather than breaking it down into several code blocks. For certain types of code, they can render graphical interfaces in a UI window. After generation, please check the code execution again to ensure there are no errors in the output.
-Output only the HTML, without any additional descriptive text. Make the UI looks modern and beautiful.
-```
-#### C. Mathematical Scenarios
-When dealing with problems that require calculation or logical deduction:
-```
-Please reason step by step, and put your final answer within \boxed{}.
-```
-## 4. 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). Special Note: Using vLLM versions below 0.9.2 may result in incompatibility or incorrect precision for the model.
-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).
-## 5. 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.
-## 6. 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.
-## 7. Citation
-```
-@misc{minimax2025minimaxm1scalingtesttimecompute,
-      title={MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention},
-      author={MiniMax},
-      year={2025},
-      eprint={2506.13585},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2506.13585},
-}
-```
-## 8. Contact Us
 Contact us at [[email protected]](mailto:[email protected]).

+---
+pipeline_tag: text-generation
+license: apache-2.0
+---
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+  </a>
+  <a href="https://chat.minimax.io/" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/_MiniMax_Chat-FF4040?style=flat-square&labelColor=2C3E50&logo=data:image/svg+xml;base64,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&logoWidth=20" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://www.minimax.io/platform" style="margin: 2px;">
+    <img alt="API" src="https://img.shields.io/badge/⚡_API-Platform-FF4040?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;"/>
+  </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;"/>
+  </a>
+</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;"/>
+  </a>
+  <a href="https://github.com/MiniMax-AI/MiniMax-M1" target="_blank" style="margin: 2px;">
+    <img alt="GitHub" src="https://img.shields.io/badge/🐙_GitHub-MiniMax-FF4040?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://www.modelscope.cn/organization/MiniMax" target="_blank" style="margin: 2px;">
+    <img alt="ModelScope" src="https://img.shields.io/badge/🤖️_ModelScope-MiniMax-FF4040?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/MiniMax-AI/MiniMax-M1/blob/main/LICENSE" style="margin: 2px;">
+    <img alt="License" src="https://img.shields.io/badge/⚖️_License-Apache_2.0-FF4040?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/MiniMax-AI/MiniMax-01/blob/main/figures/wechat-qrcode.jpeg" target="_blank" style="margin: 2px;">
+    <img alt="WeChat" src="https://img.shields.io/badge/💬_WeChat-MiniMax-FF4040?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+# 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. Recommendations for Minimax-M1 Model Usage
+To achieve the best results with the Minimax-M1 model, we suggest focusing on two key points: Inference Parameters and the System Prompt.
+### 3.1. Inference Parameters
+- Temperature: **`1.0`**
+- Top_p: **`0.95`**
+This setting is optimal for encouraging creativity and diversity in the model's responses. It allows the model to explore a wider range of linguistic possibilities, preventing outputs that are too rigid or repetitive, while still maintaining strong logical coherence.
+### 3.2. System Prompt
+Tailoring your system prompt to the specific task is crucial for guiding the model effectively. Below are suggested settings for different scenarios.
+#### A. General-Purpose Scenarios
+For common tasks like summarization, translation, Q&A, or creative writing:
+```
+You are a helpful assistant.
+```
+#### B. Web Development Scenarios
+For complex tasks like generating code for web pages:
+```
+You are a web development engineer, writing web pages according to the instructions below. You are a powerful code editing assistant capable of writing code and creating artifacts in conversations with users, or modifying and updating existing artifacts as requested by users.
+All code is written in a single code block to form a complete code file for display, without separating HTML and JavaScript code. An artifact refers to a runnable complete code snippet, you prefer to integrate and output such complete runnable code rather than breaking it down into several code blocks. For certain types of code, they can render graphical interfaces in a UI window. After generation, please check the code execution again to ensure there are no errors in the output.
+Output only the HTML, without any additional descriptive text. Make the UI looks modern and beautiful.
+```
+#### C. Mathematical Scenarios
+When dealing with problems that require calculation or logical deduction:
+```
+Please reason step by step, and put your final answer within \boxed{}.
+```
+## 4. 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).
+## 5. 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.
+## 6. 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.
+## 7. Citation
+```
+@misc{minimax2025minimaxm1scalingtesttimecompute,
+      title={MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention},
+      author={MiniMax},
+      year={2025},
+      eprint={2506.13585},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2506.13585},
+}
+```
+## 8. Contact Us
 Contact us at [[email protected]](mailto:[email protected]).

docs/function_call_guide.md CHANGED Viewed

@@ -8,122 +8,9 @@ The MiniMax-M1 model supports function calling capabilities, enabling the model
 ## 🚀 Quick Start
-### Using vLLM for Function Calls (Recommended)
-In actual deployment, to support native Function Calling (tool calling) capabilities similar to OpenAI API, the MiniMax-M1 model integrates a dedicated `tool_call_parser=minimax` parser, avoiding additional regex parsing of model output.
-#### Environment Setup and vLLM Recompilation
-Since this feature has not been officially released in the PyPI version, compilation from source code is required. The following is an example process based on the official vLLM Docker image `vllm/vllm-openai:v0.8.3`:
-```bash
-IMAGE=vllm/vllm-openai:v0.8.3
-DOCKER_RUN_CMD="--network=host --privileged --ipc=host --ulimit memlock=-1 --shm-size=32gb --rm --gpus all --ulimit stack=67108864"
-# Run docker
-sudo docker run -it -v $MODEL_DIR:$MODEL_DIR \
-                    -v $CODE_DIR:$CODE_DIR \
-                    --name vllm_function_call \
-                    $DOCKER_RUN_CMD \
-                    --entrypoint /bin/bash \
-                    $IMAGE
-```
-#### Compiling vLLM Source Code
-After entering the container, execute the following commands to get the source code and reinstall:
-```bash
-cd $CODE_DIR
-git clone https://github.com/vllm-project/vllm.git
-cd vllm
-pip install -e .
-```
-#### Starting vLLM API Service
-```bash
-export SAFETENSORS_FAST_GPU=1
-export VLLM_USE_V1=0
-python3 -m vllm.entrypoints.openai.api_server \
---model MiniMax-M1-80k \
---tensor-parallel-size 8 \
---trust-remote-code \
---quantization experts_int8  \
---enable-auto-tool-choice \
---tool-call-parser minimax \
---chat-template vllm/examples/tool_chat_template_minimax_m1.jinja \
---max_model_len 4096 \
---dtype bfloat16 \
---gpu-memory-utilization 0.85
-```
-**⚠️ Note:**
-- `--tool-call-parser minimax` is a key parameter for enabling the MiniMax-M1 custom parser
-- `--enable-auto-tool-choice` enables automatic tool selection
-- `--chat-template` template file needs to be adapted for tool calling format
-#### Function Call Test Script Example
-The following Python script implements a weather query function call example based on OpenAI SDK:
-```python
-from openai import OpenAI
-import json
-client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")
-def get_weather(location: str, unit: str):
-    return f"Getting the weather for {location} in {unit}..."
-tool_functions = {"get_weather": get_weather}
-tools = [{
-    "type": "function",
-    "function": {
-        "name": "get_weather",
-        "description": "Get the current weather in a given location",
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "location": {"type": "string", "description": "City and state, e.g., 'San Francisco, CA'"},
-                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
-            },
-            "required": ["location", "unit"]
-        }
-    }
-}]
-response = client.chat.completions.create(
-    model=client.models.list().data[0].id,
-    messages=[{"role": "user", "content": "What's the weather like in San Francisco? use celsius."}],
-    tools=tools,
-    tool_choice="auto"
-)
-print(response)
-tool_call = response.choices[0].message.tool_calls[0].function
-print(f"Function called: {tool_call.name}")
-print(f"Arguments: {tool_call.arguments}")
-print(f"Result: {get_weather(**json.loads(tool_call.arguments))}")
-```
-**Output Example:**
-```
-Function called: get_weather
-Arguments: {"location": "San Francisco, CA", "unit": "celsius"}
-Result: Getting the weather for San Francisco, CA in celsius...
-```
-### Manual Parsing of Model Output
-If you cannot use vLLM's built-in parser, or need to use other inference frameworks (such as transformers, TGI, etc.), you can use the following method to manually parse the model's raw output. This method requires you to parse the XML tag format of the model output yourself.
-#### Using Transformers Example
-The following is a complete example using the transformers library:
 ```python
 from transformers import AutoTokenizer
@@ -131,19 +18,21 @@ from transformers import AutoTokenizer
 def get_default_tools():
     return [
         {
-          "name": "get_current_weather",
-          "description": "Get the latest weather for a location",
-          "parameters": {
-              "type": "object",
-              "properties": {
-                  "location": {
-                      "type": "string",
-                      "description": "A certain city, such as Beijing, Shanghai"
-                  }
-              },
           }
-          "required": ["location"],
-          "type": "object"
         }
     ]
@@ -165,27 +54,6 @@ text = tokenizer.apply_chat_template(
     add_generation_prompt=True,
     tools=tools
 )
-# Send request (using any inference service here)
-import requests
-payload = {
-    "model": "MiniMaxAI/MiniMax-M1-40k",
-    "prompt": text,
-    "max_tokens": 4000
-}
-response = requests.post(
-    "http://localhost:8000/v1/completions",
-    headers={"Content-Type": "application/json"},
-    json=payload,
-    stream=False,
-)
-# Model output needs manual parsing
-raw_output = response.json()["choices"][0]["text"]
-print("Raw output:", raw_output)
-# Use the parsing function below to process the output
-function_calls = parse_function_calls(raw_output)
 ```
 ## 🛠️ Function Call Definition
@@ -234,21 +102,22 @@ Function calls need to be defined in the `tools` field of the request body. Each
 When processed internally by the model, function definitions are converted to a special format and concatenated to the input text:
 ```
-<begin_of_document><beginning_of_sentence>system ai_setting=MiniMax AI
-MiniMax AI是由上海稀宇科技有限公司（MiniMax）自主研发的AI助理。<end_of_sentence>
-<beginning_of_sentence>system tool_setting=tools
 You are provided with these tools:
 <tools>
-{"name": "search_web", "description": "搜索函数。", "parameters": {"properties": {"query_list": {"description": "进行搜索的关键词，列表元素个数为1。", "items": {"type": "string"}, "type": "array"}, "query_tag": {"description": "query的分类", "items": {"type": "string"}, "type": "array"}}, "required": ["query_list", "query_tag"], "type": "object"}}
 </tools>
 If you need to call tools, please respond with <tool_calls></tool_calls> XML tags, and provide tool-name and json-object of arguments, following the format below:
 <tool_calls>
 {"name": <tool-name>, "arguments": <args-json-object>}
 ...
-</tool_calls><end_of_sentence>
-<beginning_of_sentence>user name=用户
-OpenAI 和 Gemini 的最近一次发布会都是什么时候?<end_of_sentence>
-<beginning_of_sentence>ai name=MiniMax AI
 ```
 ### Model Output Format
@@ -265,15 +134,16 @@ Okay, I will search for the OpenAI and Gemini latest release.
 </tool_calls>
 ```
-## 📥 Manual Parsing of Function Call Results
 ### Parsing Function Calls
-When manual parsing is required, you need to parse the XML tag format of the model output:
 ```python
 import re
 import json
 def parse_function_calls(content: str):
     """
     Parse function calls from model output
@@ -323,33 +193,23 @@ def execute_function_call(function_name: str, arguments: dict):
         # Build function execution result
         return {
             "role": "tool",
-            "content": [
-              {
-                "name": function_name,
-                "type": "text",
-                "text": json.dumps({
-                    "location": location,
-                    "temperature": "25",
-                    "unit": "celsius",
-                    "weather": "Sunny"
-                }, ensure_ascii=False)
-              }
-            ]
-          }
     elif function_name == "search_web":
         query_list = arguments.get("query_list", [])
         query_tag = arguments.get("query_tag", [])
         # Simulate search results
         return {
             "role": "tool",
-            "content": [
-              {
-                "name": function_name,
-                "type": "text",
-                "text": f"Search keywords: {query_list}, Categories: {query_tag}\nSearch results: Relevant information found"
-              }
-            ]
-          }
     return None
 ```
@@ -360,65 +220,51 @@ After successfully parsing function calls, you should add the function execution
 #### Single Result
-If the model calls the `search_web` function, you can refer to the following format to add execution results, with the `name` field being the specific function name.
 ```json
 {
-  "role": "tool",
-  "content": [
-    {
-      "name": "search_web",
-      "type": "text",
-      "text": "test_result"
-    }
   ]
 }
 ```
 Corresponding model input format:
 ```
-<beginning_of_sentence>tool name=tools
-tool name: search_web
-tool result: test_result
-<end_of_sentence>
 ```
-#### Multiple Results
-If the model calls both `search_web` and `get_current_weather` functions simultaneously, you can refer to the following format to add execution results, with `content` containing multiple results.
 ```json
 {
-  "role": "tool",
-  "content": [
-    {
-      "name": "search_web",
-      "type": "text",
-      "text": "test_result1"
-    },
-    {
-      "name": "get_current_weather",
-      "type": "text",
-      "text": "test_result2"
-    }
   ]
 }
 ```
 Corresponding model input format:
 ```
-<beginning_of_sentence>tool name=tools
-tool name: search_web
-tool result: test_result1
-tool name: get_current_weather
-tool result: test_result2<end_of_sentence>
-```
-While we recommend following the above formats, as long as the input returned to the model is easy to understand, the specific content of `name` and `text` is entirely up to you.
-## 📚 References
-- [MiniMax-M1 Model Repository](https://github.com/MiniMaxAI/MiniMax-M1)
-- [vLLM Project Homepage](https://github.com/vllm-project/vllm)
-- [vLLM Function Calling PR](https://github.com/vllm-project/vllm/pull/20297)
-- [OpenAI Python SDK](https://github.com/openai/openai-python)

 ## 🚀 Quick Start
+### Using Chat Template
+MiniMax-M1 uses a specific chat template format to handle function calls. The chat template is defined in `tokenizer_config.json`, and you can use it in your code through the template.
 ```python
 from transformers import AutoTokenizer
 def get_default_tools():
     return [
         {
+          {
+            "name": "get_current_weather",
+            "description": "Get the latest weather for a location",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "A certain city, such as Beijing, Shanghai"
+                    }
+                },
+            }
+            "required": ["location"],
+            "type": "object"
           }
         }
     ]
     add_generation_prompt=True,
     tools=tools
 )
 ```
 ## 🛠️ Function Call Definition
 When processed internally by the model, function definitions are converted to a special format and concatenated to the input text:
 ```
+]~!b[]~b]system ai_setting=MiniMax AI
+MiniMax AI is an AI assistant independently developed by MiniMax. [e~[
+]~b]system tool_setting=tools
 You are provided with these tools:
 <tools>
+{"name": "search_web", "description": "Search function.", "parameters": {"properties": {"query_list": {"description": "Keywords for search, with list element count of 1.", "items": {"type": "string"}, "type": "array"}, "query_tag": {"description": "Classification of the query", "items": {"type": "string"}, "type": "array"}}, "required": ["query_list", "query_tag"], "type": "object"}}
 </tools>
 If you need to call tools, please respond with <tool_calls></tool_calls> XML tags, and provide tool-name and json-object of arguments, following the format below:
 <tool_calls>
 {"name": <tool-name>, "arguments": <args-json-object>}
 ...
+</tool_calls>[e~[
+]~b]user name=User
+When were the most recent launch events for OpenAI and Gemini?[e~[
+]~b]ai name=MiniMax AI
 ```
 ### Model Output Format
 </tool_calls>
 ```
+## 📥 Function Call Result Processing
 ### Parsing Function Calls
+You can use the following code to parse function calls from the model output:
 ```python
 import re
 import json
 def parse_function_calls(content: str):
     """
     Parse function calls from model output
         # Build function execution result
         return {
             "role": "tool",
+            "name": function_name,
+            "content": json.dumps({
+                "location": location,
+                "temperature": "25",
+                "unit": "celsius",
+                "weather": "Sunny"
+            }, ensure_ascii=False)
+        }
     elif function_name == "search_web":
         query_list = arguments.get("query_list", [])
         query_tag = arguments.get("query_tag", [])
         # Simulate search results
         return {
             "role": "tool",
+            "name": function_name,
+            "content": f"Search keywords: {query_list}, Categories: {query_tag}\nSearch results: Relevant information found"
+        }
     return None
 ```
 #### Single Result
+If the model decides to call `search_web`, we suggest you to return the function result in the following format, with the `name` field set to the specific tool name.
 ```json
 {
+  "data": [
+     {
+       "role": "tool",
+       "name": "search_web",
+       "content": "search_result"
+     }
   ]
 }
 ```
 Corresponding model input format:
 ```
+]~b]tool name=search_web
+search_result[e~[
 ```
+#### Multiple Result
+If the model decides to call `search_web` and `get_current_weather` at the same time, we suggest you to return the multiple function results in the following format, with the `name` field set to "tools", and use the `content` field to contain multiple results.
 ```json
 {
+  "data": [
+     {
+       "role": "tool",
+       "name": "tools",
+       "content": "Tool name: search_web\nTool result: test_result1\n\nTool name: get_current_weather\nTool result: test_result2"
+     }
   ]
 }
 ```
 Corresponding model input format:
 ```
+]~b]tool name=tools
+Tool name: search_web
+Tool result: test_result1
+Tool name: get_current_weather
+Tool result: test_result2[e~[
+```
+While we suggest following the above formats, as long as the model input is easy to understand, the specific values of `name` and `content` is entirely up to the caller.

docs/function_call_guide_cn.md CHANGED Viewed

@@ -6,122 +6,9 @@ MiniMax-M1 模型支持函数调用功能，使模型能够识别何时需要调
 ## 🚀 快速开始
-### 使用 vLLM 进行 Function Calls（推荐）
-在实际部署过程中，为了支持类似 OpenAI API 的原生 Function Calling（工具调用）能力，MiniMax-M1 模型集成了专属 `tool_call_parser=minimax` 解析器，从而避免对模型输出结果进行额外的正则解析处理。
-#### 环境准备与重新编译 vLLM
-由于该功能尚未正式发布在 PyPI 版本中，需基于源码进行编译。以下为基于 vLLM 官方 Docker 镜像 `vllm/vllm-openai:v0.8.3` 的示例流程：
-```bash
-IMAGE=vllm/vllm-openai:v0.8.3
-DOCKER_RUN_CMD="--network=host --privileged --ipc=host --ulimit memlock=-1 --shm-size=32gb --rm --gpus all --ulimit stack=67108864"
-# 运行 docker
-sudo docker run -it -v $MODEL_DIR:$MODEL_DIR \
-                    -v $CODE_DIR:$CODE_DIR \
-                    --name vllm_function_call \
-                    $DOCKER_RUN_CMD \
-                    --entrypoint /bin/bash \
-                    $IMAGE
-```
-#### 编译 vLLM 源码
-进入容器后，执行以下命令以获取源码并重新安装：
-```bash
-cd $CODE_DIR
-git clone https://github.com/vllm-project/vllm.git
-cd vllm
-pip install -e .
-```
-#### 启动 vLLM API 服务
-```bash
-export SAFETENSORS_FAST_GPU=1
-export VLLM_USE_V1=0
-python3 -m vllm.entrypoints.openai.api_server \
---model MiniMax-M1-80k \
---tensor-parallel-size 8 \
---trust-remote-code \
---quantization experts_int8  \
---enable-auto-tool-choice \
---tool-call-parser minimax \
---chat-template vllm/examples/tool_chat_template_minimax_m1.jinja \
---max_model_len 4096 \
---dtype bfloat16 \
---gpu-memory-utilization 0.85
-```
-**⚠️ 注意：**
-- `--tool-call-parser minimax` 为关键参数，用于启用 MiniMax-M1 自定义解析器
-- `--enable-auto-tool-choice` 启用自动工具选择
-- `--chat-template` 模板文件需要适配 tool calling 格式
-#### Function Call 测试脚本示例
-以下 Python 脚本基于 OpenAI SDK 实现了一个天气查询函数的调用示例：
-```python
-from openai import OpenAI
-import json
-client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")
-def get_weather(location: str, unit: str):
-    return f"Getting the weather for {location} in {unit}..."
-tool_functions = {"get_weather": get_weather}
-tools = [{
-    "type": "function",
-    "function": {
-        "name": "get_weather",
-        "description": "Get the current weather in a given location",
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "location": {"type": "string", "description": "City and state, e.g., 'San Francisco, CA'"},
-                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
-            },
-            "required": ["location", "unit"]
-        }
-    }
-}]
-response = client.chat.completions.create(
-    model=client.models.list().data[0].id,
-    messages=[{"role": "user", "content": "What's the weather like in San Francisco? use celsius."}],
-    tools=tools,
-    tool_choice="auto"
-)
-print(response)
-tool_call = response.choices[0].message.tool_calls[0].function
-print(f"Function called: {tool_call.name}")
-print(f"Arguments: {tool_call.arguments}")
-print(f"Result: {get_weather(**json.loads(tool_call.arguments))}")
-```
-**输出示例：**
-```
-Function called: get_weather
-Arguments: {"location": "San Francisco, CA", "unit": "celsius"}
-Result: Getting the weather for San Francisco, CA in celsius...
-```
-### 手动解析模型输出
-如果您无法使用 vLLM 的内置解析器，或者需要使用其他推理框架（如 transformers、TGI 等），可以使用以下方法手动解析模型的原始输出。这种方法需要您自己解析模型输出的 XML 标签格式。
-#### 使用 Transformers 的示例
-以下是使用 transformers 库的完整示例：
 ```python
 from transformers import AutoTokenizer
@@ -129,19 +16,21 @@ from transformers import AutoTokenizer
 def get_default_tools():
     return [
         {
-          "name": "get_current_weather",
-          "description": "Get the latest weather for a location",
-          "parameters": {
-              "type": "object",
-              "properties": {
-                  "location": {
-                      "type": "string",
-                      "description": "A certain city, such as Beijing, Shanghai"
-                  }
-              },
           }
-          "required": ["location"],
-          "type": "object"
         }
     ]
@@ -163,27 +52,6 @@ text = tokenizer.apply_chat_template(
     add_generation_prompt=True,
     tools=tools
 )
-# 发送请求（这里使用任何推理服务）
-import requests
-payload = {
-    "model": "MiniMaxAI/MiniMax-M1-40k",
-    "prompt": text,
-    "max_tokens": 4000
-}
-response = requests.post(
-    "http://localhost:8000/v1/completions",
-    headers={"Content-Type": "application/json"},
-    json=payload,
-    stream=False,
-)
-# 模型输出需要手动解析
-raw_output = response.json()["choices"][0]["text"]
-print("原始输出:", raw_output)
-# 使用下面的解析函数处理输出
-function_calls = parse_function_calls(raw_output)
 ```
 ## 🛠️ 函数调用的定义
@@ -232,21 +100,22 @@ function_calls = parse_function_calls(raw_output)
 在模型内部处理时，函数定义会被转换为特殊格式并拼接到输入文本中：
 ```
-<begin_of_document><beginning_of_sentence>system ai_setting=MiniMax AI
-MiniMax AI是由上海稀宇科技有限公司（MiniMax）自主研发的AI助理。<end_of_sentence>
-<beginning_of_sentence>system tool_setting=tools
 You are provided with these tools:
 <tools>
 {"name": "search_web", "description": "搜索函数。", "parameters": {"properties": {"query_list": {"description": "进行搜索的关键词，列表元素个数为1。", "items": {"type": "string"}, "type": "array"}, "query_tag": {"description": "query的分类", "items": {"type": "string"}, "type": "array"}}, "required": ["query_list", "query_tag"], "type": "object"}}
 </tools>
 If you need to call tools, please respond with <tool_calls></tool_calls> XML tags, and provide tool-name and json-object of arguments, following the format below:
 <tool_calls>
 {"name": <tool-name>, "arguments": <args-json-object>}
 ...
-</tool_calls><end_of_sentence>
-<beginning_of_sentence>user name=用户
-OpenAI 和 Gemini 的最近一次发布会都是什么时候?<end_of_sentence>
-<beginning_of_sentence>ai name=MiniMax AI
 ```
 ### 模型输出格式
@@ -263,15 +132,16 @@ Okay, I will search for the OpenAI and Gemini latest release.
 </tool_calls>
 ```
-## 📥 手动解析函数调用结果
 ### 解析函数调用
-当需要手动解析时，您需要解析模型输出的 XML 标签格式：
 ```python
 import re
 import json
 def parse_function_calls(content: str):
     """
     解析模型输出中的函数调用
@@ -321,33 +191,23 @@ def execute_function_call(function_name: str, arguments: dict):
         # 构建函数执行结果
         return {
             "role": "tool",
-            "content": [
-              {
-                "name": function_name,
-                "type": "text",
-                "text": json.dumps({
-                    "location": location,
-                    "temperature": "25",
-                    "unit": "celsius",
-                    "weather": "晴朗"
-                }, ensure_ascii=False)
-              }
-            ]
-          }
     elif function_name == "search_web":
         query_list = arguments.get("query_list", [])
         query_tag = arguments.get("query_tag", [])
         # 模拟搜索结果
         return {
             "role": "tool",
-            "content": [
-              {
-                "name": function_name,
-                "type": "text",
-                "text": f"搜索关键词: {query_list}, 分类: {query_tag}\n搜索结果: 相关信息已找到"
-              }
-            ]
-          }
     return None
 ```
@@ -362,61 +222,46 @@ def execute_function_call(function_name: str, arguments: dict):
 ```json
 {
-  "role": "tool",
-  "content": [
-    {
-      "name": "search_web",
-      "type": "text",
-      "text": "test_result"
-    }
   ]
 }
 ```
 对应如下的模型输入格式：
 ```
-<beginning_of_sentence>tool name=tools
-tool name: search_web
-tool result: test_result
-<end_of_sentence>
 ```
-#### 多个结果
-假如模型同时调用了 `search_web` 和 `get_current_weather` 函数，您可以参考如下格式添加执行结果，`content`包含多个结果。
 ```json
 {
-  "role": "tool",
-  "content": [
-    {
-      "name": "search_web",
-      "type": "text",
-      "text": "test_result1"
-    },
-    {
-      "name": "get_current_weather",
-      "type": "text",
-      "text": "test_result2"
-    }
   ]
 }
 ```
 对应如下的模型输入格式：
 ```
-<beginning_of_sentence>tool name=tools
-tool name: search_web
-tool result: test_result1
-tool name: get_current_weather
-tool result: test_result2<end_of_sentence>
-```
-虽然我们建议您参考以上格式，但只要返回给模型的输入易于理解，`name` 和 `text` 的具体内容完全由您自主决定。
-## 📚 参考资料
-- [MiniMax-M1 模型仓库](https://github.com/MiniMaxAI/MiniMax-M1)
-- [vLLM 项目主页](https://github.com/vllm-project/vllm)
-- [vLLM Function Calling PR](https://github.com/vllm-project/vllm/pull/20297)
-- [OpenAI Python SDK](https://github.com/openai/openai-python)

 ## 🚀 快速开始
+### 聊天模板使用
+MiniMax-M1 使用特定的聊天模板格式处理函数调用。聊天模板定义在 `tokenizer_config.json` 中，你可以在代码中通过 template 来进行使用。
 ```python
 from transformers import AutoTokenizer
 def get_default_tools():
     return [
         {
+          {
+            "name": "get_current_weather",
+            "description": "Get the latest weather for a location",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "A certain city, such as Beijing, Shanghai"
+                    }
+                },
+            }
+            "required": ["location"],
+            "type": "object"
           }
         }
     ]
     add_generation_prompt=True,
     tools=tools
 )
 ```
 ## 🛠️ 函数调用的定义
 在模型内部处理时，函数定义会被转换为特殊格式并拼接到输入文本中：
 ```
+]~!b[]~b]system ai_setting=MiniMax AI
+MiniMax AI是由上海稀宇科技有限公司（MiniMax）自主研发的AI助理。[e~[
+]~b]system tool_setting=tools
 You are provided with these tools:
 <tools>
 {"name": "search_web", "description": "搜索函数。", "parameters": {"properties": {"query_list": {"description": "进行搜索的关键词，列表元素个数为1。", "items": {"type": "string"}, "type": "array"}, "query_tag": {"description": "query的分类", "items": {"type": "string"}, "type": "array"}}, "required": ["query_list", "query_tag"], "type": "object"}}
 </tools>
 If you need to call tools, please respond with <tool_calls></tool_calls> XML tags, and provide tool-name and json-object of arguments, following the format below:
 <tool_calls>
 {"name": <tool-name>, "arguments": <args-json-object>}
 ...
+</tool_calls>[e~[
+]~b]user name=用户
+OpenAI 和 Gemini 的最近一次发布会都是什么时候?[e~[
+]~b]ai name=MiniMax AI
 ```
 ### 模型输出格式
 </tool_calls>
 ```
+## 📥 函数调用结果处理
 ### 解析函数调用
+您可以使用以下代码解析模型输出的函数调用：
 ```python
 import re
 import json
 def parse_function_calls(content: str):
     """
     解析模型输出中的函数调用
         # 构建函数执行结果
         return {
             "role": "tool",
+            "name": function_name,
+            "content": json.dumps({
+                "location": location,
+                "temperature": "25",
+                "unit": "celsius",
+                "weather": "晴朗"
+            }, ensure_ascii=False)
+        }
     elif function_name == "search_web":
         query_list = arguments.get("query_list", [])
         query_tag = arguments.get("query_tag", [])
         # 模拟搜索结果
         return {
             "role": "tool",
+            "name": function_name,
+            "content": f"搜索关键词: {query_list}, 分类: {query_tag}\n搜索结果: 相关信息已找到"
+        }
     return None
 ```
 ```json
 {
+  "data": [
+     {
+       "role": "tool",
+       "name": "search_web",
+       "content": "search_result"
+     }
   ]
 }
 ```
 对应如下的模型输入格式：
 ```
+]~b]tool name=search_web
+search_result[e~[
 ```
+#### 多个结果
+假如模型同时调用了 `search_web` 和 `get_current_weather` 函数，您可以参考如下格式添加执行结果，`name` 字段为"tools"，`content`包含多个结果。
 ```json
 {
+  "data": [
+     {
+       "role": "tool",
+       "name": "tools",
+       "content": "Tool name: search_web\nTool result: test_result1\n\nTool name: get_current_weather\nTool result: test_result2"
+     }
   ]
 }
 ```
 对应如下的模型输入格式：
 ```
+]~b]tool name=tools
+Tool name: search_web
+Tool result: test_result1
+Tool name: get_current_weather
+Tool result: test_result2[e~[
+```
+虽然我们建议您参考以上格式，但只要返回给模型的输入易于理解，`name` 和 `content` 的具体内容完全由您自主决定。

docs/vllm_deployment_guide.md CHANGED Viewed

@@ -41,19 +41,20 @@ git clone https://huggingface.co/MiniMaxAI/MiniMax-M1-80k
 ## 🛠️ Deployment Options
-### Option: Deploy Using Docker (Recommended)
 To ensure consistency and stability of the deployment environment, we recommend using Docker for deployment.
 ⚠️ **Version Requirements**:
-- MiniMax-M1 model requires vLLM version 0.9.2 or later for full support
-- Special Note: Using vLLM versions below 0.9.2 may result in incompatibility or incorrect precision for the model:
-  - For details, see: [Fix minimax model cache & lm_head precision #19592](https://github.com/vllm-project/vllm/pull/19592)
 1. Get the container image:
-Currently, the official vLLM Docker image for version v0.9.2 has not been released yet.
-As an example, we will demonstrate how to manually build vLLM using version v0.8.3.
 ```bash
 docker pull vllm/vllm-openai:v0.8.3
 ```
@@ -76,12 +77,21 @@ sudo docker run -it \
     --name $NAME \
     $DOCKER_RUN_CMD \
     $IMAGE /bin/bash
-# install vLLM
-cd $CODE_DIR
-git clone https://github.com/vllm-project/vllm.git
-cd vllm
-pip install -e .
 ```
 💡 If you are using other environment configurations, please refer to the [vLLM Installation Guide](https://docs.vllm.ai/en/latest/getting_started/installation.html)

 ## 🛠️ Deployment Options
+### Option 1: Deploy Using Docker (Recommended)
 To ensure consistency and stability of the deployment environment, we recommend using Docker for deployment.
 ⚠️ **Version Requirements**:
+- MiniMax-M1 model requires vLLM version 0.8.3 or later for full support
+- If you are using a Docker image with vLLM version lower than the required version, you will need to:
+  1. Update to the latest vLLM code
+  2. Recompile vLLM from source. Follow the compilation instructions in Solution 2 of the Common Issues section
+- Special Note: For vLLM versions between 0.8.3 and 0.9.2, you need to modify the model configuration:
+  1. Open `config.json`
+  2. Change `config['architectures'] = ["MiniMaxM1ForCausalLM"]` to `config['architectures'] = ["MiniMaxText01ForCausalLM"]`
 1. Get the container image:
 ```bash
 docker pull vllm/vllm-openai:v0.8.3
 ```
     --name $NAME \
     $DOCKER_RUN_CMD \
     $IMAGE /bin/bash
+```
+### Option 2: Direct Installation of vLLM
+If your environment meets the following requirements:
+- CUDA 12.1
+- PyTorch 2.1
+You can directly install vLLM
+Installation command:
+```bash
+pip install vllm
 ```
 💡 If you are using other environment configurations, please refer to the [vLLM Installation Guide](https://docs.vllm.ai/en/latest/getting_started/installation.html)

docs/vllm_deployment_guide_cn.md CHANGED Viewed

@@ -39,18 +39,17 @@ git clone https://huggingface.co/MiniMaxAI/MiniMax-M1-80k
 ## 🛠️ 部署方案
-### 方案：使用 Docker 部署（推荐）
 为确保部署环境的一致性和稳定性，我们推荐使用 Docker 进行部署。
 ⚠️ **版本要求**：
-- 基础要求：vLLM 版本必须 ≥ 0.9.2，以确保对 MiniMax-M1 模型的完整支持
-- 特殊说明：如果使用低于 0.9.2 的 vLLM 版本，会遇见无法支持该模型或者精度不正确的情况：
-  - 详情见：[Fix minimax model cache & lm_head precision #19592](https://github.com/vllm-project/vllm/pull/19592)
 1. 获取容器镜像：
-目前 vLLM 官方还未推出v0.9.2版本 docker，我们以 v0.8.3 为例子进行手动编译 vLLM：
 ```bash
 docker pull vllm/vllm-openai:v0.8.3
 ```
@@ -73,12 +72,21 @@ sudo docker run -it \
     --name $NAME \
     $DOCKER_RUN_CMD \
     $IMAGE /bin/bash
-# 编译 vLLM
-cd $CODE_DIR
-git clone https://github.com/vllm-project/vllm.git
-cd vllm
-pip install -e .
 ```
 💡 如果您使用其他环境配置，请参考 [vLLM 安装指南](https://docs.vllm.ai/en/latest/getting_started/installation.html)

 ## 🛠️ 部署方案
+### 方案一：使用 Docker 部署（推荐）
 为确保部署环境的一致性和稳定性，我们推荐使用 Docker 进行部署。
 ⚠️ **版本要求**：
+- 基础要求：vLLM 版本必须 ≥ 0.8.3，以确保对 MiniMax-M1 模型的完整支持
+- 特殊说明：如果使用 vLLM 0.8.3 至 0.9.2 之间的版本，需要修改模型配置文件：
+  - 打开 `config.json`
+  - 将 `config['architectures'] = ["MiniMaxM1ForCausalLM"]` 修改为 `config['architectures'] = ["MiniMaxText01ForCausalLM"]`
 1. 获取容器镜像：
 ```bash
 docker pull vllm/vllm-openai:v0.8.3
 ```
     --name $NAME \
     $DOCKER_RUN_CMD \
     $IMAGE /bin/bash
+```
+### 方案二：直接安装 vLLM
+如果您的环境满足以下要求：
+- CUDA 12.1
+- PyTorch 2.1
+可以直接安装 vLLM
+安装命令：
+```bash
+pip install vllm
 ```
 💡 如果您使用其他环境配置，请参考 [vLLM 安装指南](https://docs.vllm.ai/en/latest/getting_started/installation.html)

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