MiniMax-M1

- 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.

You are a helpful assistant.

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.

Please reason step by step, and put your final answer within \boxed{}.

Input:  "The future of AI is"
Output: "The future of AI is likely to be shaped by advances in machine learning,
         natural language processing, and robotics. These technologies will enable
         new applications across industries, from healthcare to finance, and will
         continue to transform how we live and work."

Input:  "Solve: If x + 5 = 12, what is x?"
Output: "To solve for x: x + 5 = 12, subtract 5 from both sides: x = 12 - 5 = 7.
         The answer is \boxed{7}"

Input:  "Write a Python function to calculate factorial"
Output: "def factorial(n):
    if n < 0:
        raise ValueError(\"Factorial undefined for negative numbers\")
    if n == 0 or n == 1:
        return 1
    return n * factorial(n - 1)

# Example usage:
print(factorial(5))  # Output: 120"

# 1. Set environment variables
export VLLM_USE_MODELSCOPE=true
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True

# 2. Start vLLM server
vllm serve MiniMax/MiniMax-M1-80k \
  --host 0.0.0.0 --port 8000 \
  --tensor-parallel-size 2 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.90 \
  --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}' \
  --async-scheduling \
  --trust-remote-code

# 3. Test inference
curl -X POST http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minimax-m1",
    "prompt": "The future of AI is",
    "max_completion_tokens": 50,
    "temperature": 0
  }'

@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}, 
}