Instructions to use jedisct1/MiMo-7B-RL-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jedisct1/MiMo-7B-RL-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jedisct1/MiMo-7B-RL-GGUF",
	filename="MiMo-7B-RL-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use jedisct1/MiMo-7B-RL-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jedisct1/MiMo-7B-RL-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf jedisct1/MiMo-7B-RL-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jedisct1/MiMo-7B-RL-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf jedisct1/MiMo-7B-RL-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf jedisct1/MiMo-7B-RL-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf jedisct1/MiMo-7B-RL-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf jedisct1/MiMo-7B-RL-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jedisct1/MiMo-7B-RL-GGUF:Q4_K_M

Use Docker

docker model run hf.co/jedisct1/MiMo-7B-RL-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use jedisct1/MiMo-7B-RL-GGUF with Ollama:
```
ollama run hf.co/jedisct1/MiMo-7B-RL-GGUF:Q4_K_M
```

Unsloth Studio new

How to use jedisct1/MiMo-7B-RL-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jedisct1/MiMo-7B-RL-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jedisct1/MiMo-7B-RL-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jedisct1/MiMo-7B-RL-GGUF to start chatting

Pi new

How to use jedisct1/MiMo-7B-RL-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf jedisct1/MiMo-7B-RL-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jedisct1/MiMo-7B-RL-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jedisct1/MiMo-7B-RL-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf jedisct1/MiMo-7B-RL-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default jedisct1/MiMo-7B-RL-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use jedisct1/MiMo-7B-RL-GGUF with Docker Model Runner:
```
docker model run hf.co/jedisct1/MiMo-7B-RL-GGUF:Q4_K_M
```

Lemonade

How to use jedisct1/MiMo-7B-RL-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jedisct1/MiMo-7B-RL-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiMo-7B-RL-GGUF-Q4_K_M

List all available models

lemonade list

MiMo-7B-RL (GGUF)

This is a GGUF quantized version of 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

ollama run mimo-7b-rl-q8

With LM Studio

Load the model through the LM Studio interface
Select the GGUF file
Configure your desired settings
Start chatting!

With llama.cpp

./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.

Citation

If you use this model, please cite the original work:

@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.

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GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

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8-bit

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