MiMo
MiMo is a 7B parameter language model series developed by Xiaomi that specializes in mathematical and code reasoning capabilities, achieving performance comparable to larger models through innovative pre-training and post-training strategies.
https://github.com/XiaomiMiMo/MiMo?ref=aipure
Product Information
Updated:Jun 16, 2025
What is MiMo
MiMo is a series of language models developed by Xiaomi's LLM-Core Team that focuses on enhancing reasoning capabilities in both mathematics and code. The series includes MiMo-7B-Base (base model), MiMo-7B-RL (reinforcement learning model), MiMo-7B-SFT (supervised fine-tuned model), and MiMo-7B-RL-Zero. Despite its relatively small size of 7B parameters, MiMo demonstrates exceptional reasoning abilities that can match or exceed the performance of much larger 32B models and even compete with OpenAI's o1-mini model.
Key Features of MiMo
MiMo is a 7B parameter language model series developed by Xiaomi, specifically designed for enhanced reasoning capabilities in both mathematics and code. It includes different versions (Base, SFT, RL-Zero, and RL) trained through a combination of pre-training and post-training strategies, featuring Multiple-Token Prediction and specialized data processing techniques. The model demonstrates exceptional performance matching larger 32B models and OpenAI's o1-mini, particularly in mathematical and coding tasks.
Multiple-Token Prediction: Enhanced training objective that improves model performance and accelerates inference speed
Optimized Pre-training Pipeline: Uses multi-dimensional data filtering and synthetic reasoning data generation to increase reasoning pattern density
Advanced RL Training System: Features a Seamless Rollout Engine that provides 2.29× faster training and 1.96× faster validation through continuous rollout and asynchronous reward computation
Test Difficulty Driven Code Reward: Implements fine-grained scoring system for test cases with varying difficulty levels to provide more effective policy optimization
Use Cases of MiMo
Mathematical Problem Solving: Excels in solving complex mathematical problems, including AIME-level competitions and general math assessments
Code Development and Testing: Handles various coding tasks with high accuracy, particularly demonstrated through LiveCodeBench performance
General Reasoning Tasks: Performs well on general reasoning benchmarks like GPQA Diamond and SuperGPQA, making it suitable for logical analysis tasks
Pros
Matches performance of larger models despite smaller size (7B parameters)
Superior performance in both mathematics and coding tasks
Efficient inference through Multiple-Token Prediction
Open-source availability with multiple model variants
Cons
Requires specific vLLM fork for optimal performance
Lower performance on general language tasks compared to specialized reasoning tasks
Limited verification with other inference engines
How to Use MiMo
Download the Model: Download one of the MiMo models from Hugging Face (https://huggingface.co/XiaomiMiMo). Available models are: MiMo-7B-Base, MiMo-7B-RL-Zero, MiMo-7B-SFT, and MiMo-7B-RL
Setup Environment: Install the required dependencies. It's recommended to use Xiaomi's fork of vLLM which is based on vLLM 0.7.3 (https://github.com/XiaomiMiMo/vllm/tree/feat_mimo_mtp)
Choose Inference Method: You can use either vLLM (recommended) or HuggingFace for inference. vLLM supports MiMo's Multiple-Token Prediction (MTP) feature
For vLLM Inference: Import required libraries (vllm), initialize the LLM with model path and parameters (temperature=0.6 recommended), create conversation format with empty system prompt, and use llm.chat() to generate responses
For HuggingFace Inference: Import AutoModel and AutoTokenizer from transformers, load the model and tokenizer with trust_remote_code=True, tokenize inputs, and use model.generate() to create outputs
Configure Parameters: Use temperature=0.6 for best results. It's recommended to use an empty system prompt for optimal performance
Run Inference: Input your prompt/query and the model will generate responses. The model is particularly strong at reasoning tasks including mathematics and code
Handle Outputs: Process the generated text from the model output. For vLLM, access text through output.outputs[0].text. For HuggingFace, use tokenizer.decode() on the output
MiMo FAQs
MiMo is a series of 7B parameter language models developed by Xiaomi, specifically designed and trained for reasoning tasks. The series includes MiMo-7B-Base, MiMo-7B-RL-Zero, MiMo-7B-SFT, and MiMo-7B-RL models.
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