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---
language: en
license: mit
library_name: llama.cpp
tags:
- llama.cpp
- gguf
- quantized
- mimo
- reasoning
base_model: XiaomiMiMo/MiMo-7B-RL
base_model_relation: quantized
---
# MiMo-7B-RL (GGUF)
This is a GGUF quantized version of [XiaomiMiMo/MiMo-7B-RL](https://huggingface.co/XiaomiMiMo/MiMo-7B-RL), optimized for use with llama.cpp, Ollama, LM Studio, and other GGUF-compatible inference engines. The model has been converted from the original SafeTensors format to GGUF.
## Model Description
MiMo-7B-RL is a powerful 7B parameter language model developed by Xiaomi, specifically designed for enhanced reasoning capabilities in both mathematics and code. The original model matches the performance of OpenAI's o1-mini in many benchmarks.
### Model Details
- **Original Model**: MiMo-7B-RL by Xiaomi
- **Parameters**: 7 billion
- **Context Length**: 32,768 tokens
- **Architecture**: Modified transformer with 36 layers, 32 attention heads
- **Original Format**: SafeTensors
- **Converted Format**: GGUF
- **License**: MIT
Key features of the original model:
- Trained using a specialized pre-training strategy focused on reasoning tasks
- Fine-tuned with reinforcement learning on 130K mathematics and code problems
- Demonstrates superior performance in both mathematical reasoning and coding tasks
- Matches performance of much larger models in reasoning capabilities
## Usage
### With Ollama
```bash
ollama run mimo-7b-rl-q8
```
### With LM Studio
1. Load the model through the LM Studio interface
2. Select the GGUF file
3. Configure your desired settings
4. Start chatting!
### With llama.cpp
```bash
./main -m mimo-7b-rl-q8.gguf -n 4096
```
## Performance
The original model demonstrates impressive performance across various benchmarks:
| Benchmark | Score |
| ------------------------- | :---: |
| MATH-500 (Pass@1) | 95.8% |
| AIME 2024 (Pass@1) | 68.2% |
| AIME 2025 (Pass@1) | 55.4% |
| LiveCodeBench v5 (Pass@1) | 57.8% |
| LiveCodeBench v6 (Pass@1) | 49.3% |
_Note: Performance metrics are from the original model. The GGUF conversion may show slightly different results due to quantization._
## Limitations and Biases
The model inherits any limitations and biases present in the original MiMo-7B-RL model. Additionally:
- Q8 quantization may result in slightly reduced performance compared to the original model
- The model requires careful prompt engineering for optimal results in reasoning tasks
- Performance may vary depending on the specific GGUF inference implementation used
## Training Details
The model was trained by Xiaomi using:
- Pre-training on approximately 25 trillion tokens
- Three-stage data mixture strategy
- Multiple-Token Prediction as an additional training objective
- RL fine-tuning on 130K mathematics and code problems
For detailed training information, please refer to the [original model card](https://huggingface.co/XiaomiMiMo/MiMo-7B-RL).
## Citation
If you use this model, please cite the original work:
```bibtex
@misc{xiaomi2025mimo,
title={MiMo: Unlocking the Reasoning Potential of Language Model – From Pretraining to Posttraining},
author={{Xiaomi LLM-Core Team}},
year={2025},
primaryClass={cs.CL},
url={https://github.com/XiaomiMiMo/MiMo},
}
```
## Acknowledgments
- Original model development by Xiaomi LLM-Core Team
- GGUF conversion by Frank Denis
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