Instructions to use NANI-Nithin/north-mini-code-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 NANI-Nithin/north-mini-code-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 NANI-Nithin/north-mini-code-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/north-mini-code-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 NANI-Nithin/north-mini-code-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/north-mini-code-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 NANI-Nithin/north-mini-code-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NANI-Nithin/north-mini-code-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 NANI-Nithin/north-mini-code-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NANI-Nithin/north-mini-code-gguf:Q4_K_M

Use Docker

docker model run hf.co/NANI-Nithin/north-mini-code-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use NANI-Nithin/north-mini-code-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NANI-Nithin/north-mini-code-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": "NANI-Nithin/north-mini-code-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NANI-Nithin/north-mini-code-gguf:Q4_K_M

Ollama
How to use NANI-Nithin/north-mini-code-gguf with Ollama:
```
ollama run hf.co/NANI-Nithin/north-mini-code-gguf:Q4_K_M
```

Unsloth Studio

How to use NANI-Nithin/north-mini-code-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 NANI-Nithin/north-mini-code-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 NANI-Nithin/north-mini-code-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NANI-Nithin/north-mini-code-gguf to start chatting

How to use NANI-Nithin/north-mini-code-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/north-mini-code-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": "NANI-Nithin/north-mini-code-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use NANI-Nithin/north-mini-code-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/north-mini-code-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 "NANI-Nithin/north-mini-code-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 NANI-Nithin/north-mini-code-gguf with Docker Model Runner:
```
docker model run hf.co/NANI-Nithin/north-mini-code-gguf:Q4_K_M
```

Lemonade

How to use NANI-Nithin/north-mini-code-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NANI-Nithin/north-mini-code-gguf:Q4_K_M

Run and chat with the model

lemonade run user.north-mini-code-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NANI-Nithin/north-mini-code-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 NANI-Nithin/north-mini-code-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 NANI-Nithin/north-mini-code-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

North-Mini-Code-1.0-GGUF

GGUF conversions and quantizations of CohereLabs/North-Mini-Code-1.0 for use with:

llama.cpp
LM Studio
Ollama
Jan
KoboldCpp
Text Generation WebUI
Open WebUI
Other GGUF-compatible runtimes

About the Model

North-Mini-Code-1.0 is a code-focused Mixture-of-Experts (MoE) model released by CohereLabs.

This repository provides ready-to-use GGUF conversions for local inference across a range of hardware configurations.

Available Files

Full Precision

North-Mini-Code-1.0-F16.gguf

Quantized Versions

North-Mini-Code-1.0-Q4_K_M.gguf
North-Mini-Code-1.0-Q5_K_M.gguf
North-Mini-Code-1.0-Q6_K.gguf
North-Mini-Code-1.0-Q8_0.gguf

Recommended Quantization

For most users:

North-Mini-Code-1.0-Q4_K_M.gguf

It offers the best balance of:

Quality
Memory usage
Inference speed

If you have more available RAM/VRAM, consider:

North-Mini-Code-1.0-Q5_K_M.gguf

North-Mini-Code-1.0-Q6_K.gguf

for slightly higher output quality.

Approximate File Sizes

F16      ~60+ GB
Q4_K_M   ~20 GB
Q5_K_M   ~23 GB
Q6_K     ~27 GB
Q8_0     ~34 GB

Actual sizes may vary slightly depending on conversion tooling versions.

Usage

llama.cpp

Prompt mode:

./llama-cli \
  -m North-Mini-Code-1.0-Q4_K_M.gguf \
  -p "Write a Python function that reverses a linked list."

Chat mode:

./llama-cli \
  -m North-Mini-Code-1.0-Q4_K_M.gguf \
  -cnv

LM Studio

Download your preferred GGUF file.
Open LM Studio.
Import the model.
Start chatting.

Ollama

Create a Modelfile:

FROM North-Mini-Code-1.0-Q4_K_M.gguf

Create the model:

ollama create north-mini-code -f Modelfile

Run it:

ollama run north-mini-code

Hardware Recommendations

Q4_K_M

Recommended minimum:

24 GB RAM

Q5_K_M

Recommended minimum:

32 GB RAM

Q6_K

Recommended minimum:

32-40 GB RAM

Q8_0

Recommended minimum:

48+ GB RAM

F16

Recommended minimum:

80+ GB RAM

Prompting Tips

This model is optimized for programming-related tasks.

Example prompts:

Implement a fast Rust HTTP server.

Explain this C++ compiler error.

Write comprehensive unit tests for the following Python code.

Convert this JavaScript function to TypeScript.

Optimize this SQL query.

Base Model

Base model:

CohereLabs/North-Mini-Code-1.0

All training, architecture, benchmarks, licensing terms, and usage restrictions belong to the original model authors.

Please refer to the original repository for official documentation and licensing information.

Conversion Details

Converted using:

llama.cpp

Generated quantizations:

F16
Q4_K_M
Q5_K_M
Q6_K
Q8_0

A tokenizer compatibility workaround was applied during conversion to support current GGUF conversion tooling.

Credits

Base Model: CohereLabs
GGUF Conversion & Quantization: NANI-Nithin
Tooling: llama.cpp

Repository

👉 https://huggingface.co/NANI-Nithin/north-mini-code-gguf

Downloads last month: 95

GGUF

Model size

30B params

Architecture

cohere2moe

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for NANI-Nithin/north-mini-code-gguf

Base model

CohereLabs/North-Mini-Code-1.0

Quantized

(37)

this model