Instructions to use artindnr/mochi-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use artindnr/mochi-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf artindnr/mochi-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf artindnr/mochi-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf artindnr/mochi-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf artindnr/mochi-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 artindnr/mochi-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf artindnr/mochi-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 artindnr/mochi-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf artindnr/mochi-GGUF:Q4_K_M

Use Docker

docker model run hf.co/artindnr/mochi-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use artindnr/mochi-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "artindnr/mochi-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "artindnr/mochi-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/artindnr/mochi-GGUF:Q4_K_M

Ollama
How to use artindnr/mochi-GGUF with Ollama:
```
ollama run hf.co/artindnr/mochi-GGUF:Q4_K_M
```

Unsloth Studio

How to use artindnr/mochi-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 artindnr/mochi-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 artindnr/mochi-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for artindnr/mochi-GGUF to start chatting

How to use artindnr/mochi-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf artindnr/mochi-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": "artindnr/mochi-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use artindnr/mochi-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf artindnr/mochi-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "artindnr/mochi-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use artindnr/mochi-GGUF with Docker Model Runner:
```
docker model run hf.co/artindnr/mochi-GGUF:Q4_K_M
```

Lemonade

How to use artindnr/mochi-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull artindnr/mochi-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.mochi-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use artindnr/mochi-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf artindnr/mochi-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 artindnr/mochi-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

🍡 Mochi GGUF

GGUF quantizations of artindnr/mochi, a math-reasoning fine-tune of GLM-4.7-Flash trained on the Open Math Reasoning (mini) dataset — the same chain-of-thought data behind the winning submission to the AIMO-2 Kaggle competition.

These quants let you run Mochi locally with llama.cpp, Ollama, LM Studio, or any other GGUF-compatible runtime.

Files

File	Quant	Notes
`mochi.Q8_0.gguf`	Q8_0	Highest quality of the three, largest file size, closest to full precision
`mochi.Q5_K_M.gguf`	Q5_K_M	Balanced quality/size trade-off, good default for most setups
`mochi.Q4_K_M.gguf`	Q4_K_M	Smallest and fastest, some quality loss vs. higher-bit quants

If you're unsure which to pick: Q5_K_M is a solid default. Use Q8_0 if you have the VRAM/RAM to spare and want maximum fidelity, and Q4_K_M if you're constrained on memory or want faster inference.

Usage

llama.cpp

./llama-cli -m mochi.Q5_K_M.gguf -p "If x^2 - 5x + 6 = 0, what are the values of x?" -n 512

Or serve it as an OpenAI-compatible endpoint:

./llama-server -m mochi.Q5_K_M.gguf -c 4096

Ollama

Create a Modelfile:

FROM ./mochi.Q5_K_M.gguf

Then:

ollama create mochi -f Modelfile
ollama run mochi

LM Studio

Download the .gguf file of your choice directly in LM Studio's model browser (search artindnr/mochi-gguf), or drop the file into your local models folder.

About Mochi

Mochi is a chain-of-thought math fine-tune of GLM-4.7-Flash. See the full model card for training details, dataset info, and intended use.

Base model: GLM-4.7-Flash
Fine-tuning data: unsloth/OpenMathReasoning-mini
Focus: Step-by-step mathematical reasoning (olympiad-style problems)
Format: GGUF, for use with llama.cpp and compatible runtimes

Limitations

Quantization introduces some precision loss versus the original fp16/bf16 weights — expect small quality differences between Q8_0, Q5_K_M, and Q4_K_M, especially on harder problems.
Fine-tuned specifically for math reasoning; general chat ability may differ from the base GLM-4.7-Flash model.
Always verify important results — this is not a substitute for a calculator or formal proof checker.

Acknowledgements

GLM-4.7-Flash for the base model
Unsloth for fine-tuning tooling
NVIDIA's AIMO-2 team for the OpenMathReasoning dataset
llama.cpp for the GGUF format and quantization tooling

Downloads last month: -

GGUF

Model size

31B params

Architecture

deepseek2

Hardware compatibility

4-bit

5-bit

8-bit

Model tree for artindnr/mochi-GGUF

Base model

artindnr/mochi

Quantized

(1)

this model

Dataset used to train artindnr/mochi-GGUF

Collection including artindnr/mochi-GGUF

🍡 Mochi

Collection

A collection of Mochi models, reasoning models fine-tuned for mathematical reasoning to score high on AIME 500. • 2 items • Updated about 18 hours ago