Instructions to use FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF",
	filename="north-mini-code-1.0-mxfp4_moe.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use FreedomAISVR/North-Mini-Code-1.0-MXFP4-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 FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE

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 FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE

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 FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE

Use Docker

docker model run hf.co/FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE

LM Studio
Jan

vLLM

How to use FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomAISVR/North-Mini-Code-1.0-MXFP4-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": "FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE

Ollama
How to use FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE
```

Unsloth Studio

How to use FreedomAISVR/North-Mini-Code-1.0-MXFP4-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 FreedomAISVR/North-Mini-Code-1.0-MXFP4-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 FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF to start chatting

How to use FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE

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": "FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FreedomAISVR/North-Mini-Code-1.0-MXFP4-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 FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE

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 FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE
```

Lemonade

How to use FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF:MXFP4_MOE

Run and chat with the model

lemonade run user.North-Mini-Code-1.0-MXFP4-GGUF-MXFP4_MOE

List all available models

lemonade list

North-Mini-Code-1.0-MXFP4-GGUF

MXFP4_MOE (OCP Microscaling FP4 E2M1, block size 32) 4-bit quantization of CohereLabs/North-Mini-Code-1.0.

Model Description

North-Mini-Code-1.0 is a 30B total parameter MoE code model with 2.7B active parameters per token. It uses 128 experts with 8 selected per token, 49 transformer layers (hybrid sliding window + full attention at 3:1 ratio), and a vocabulary of 256K tokens. Architecture follows the Cohere2MoE design with parallel residual blocks, grouped-query attention (32 heads, 4 KV heads, 8:1 GQA ratio), RMS norm, and SiLU-gated activations.

Config	Value
Total parameters	~30.5B
Active parameters	~2.7B
Layers	49 (13 full + 36 sliding window, 3:1 ratio)
Attention heads	32 (4 KV heads, GQA 8:1)
Head dimension	128
Hidden dimension	2048
MLP intermediate	768 (MoE), 3072 (dense prefix)
Experts	128 (8 active per token)
Context window	4096 (sliding) / 500000 (full with RoPE)
Vocabulary	262144 tokens
RoPE theta	50000.0

MXFP4_MOE Quantization

MXFP4_MOE applies the OCP MXFP4 microscaling format (E2M1, block size 32) to expert weight tensors while keeping attention projections at Q8_0. This hybrid approach optimizes the quality-size tradeoff for MoE architectures: the 128 expert FFN layers (~97% of parameters) benefit from MXFP4 density, while attention tensors stay higher precision for better routing and context processing.

Unlike NVFP4 (NVIDIA-proprietary), MXFP4 is an open OCP standard compatible with any GPU or CPU backend that implements the microscaling specification.

Format	File Size	BPW	Block Size	Expert Format	Attention Format
MXFP4_MOE	17.04 GB	~4.8	32	MXFP4 (E2M1)	Q8_0
BF16 (original)	56.8 GB	16	1	BF16	BF16

Notes:

MXFP4 per-block shared exponents preserve dynamic range for expert weight outliers
Q8_0 attention layers maintain precision for key/value projection and output
Compatible with any GPU supporting MXFP4 via CUDA 13.x or LLVM SPIR-V; falls back to CPU for unsupported hardware
Open standard (OCP Microscaling, OCP Specification v1.0)

Files

File	Size	Description
north-mini-code-1.0-mxfp4_moe.gguf	17.04 GB	MXFP4_MOE quantized text model

Conversion Pipeline

CohereLabs/North-Mini-Code-1.0 (HF safetensors, BF16, 56.8 GB)
  -> convert_hf_to_gguf.py --outtype f16 (GGUF F16, 61.0 GB, cohere2_moe arch)
  -> llama-quantize.exe MXFP4_MOE (GGUF MXFP4_MOE, 17.04 GB, 442 tensors)

Usage

llama.cpp:

./llama-cli -m north-mini-code-1.0-mxfp4_moe.gguf -p "Write a Python function implementing merge sort with type annotations" -n 512 -t 8 -c 8192

llama-cpp-python:

from llama_cpp import Llama
llm = Llama(model_path="north-mini-code-1.0-mxfp4_moe.gguf", n_ctx=8192, n_threads=8, n_gpu_layers=-1)
output = llm("Write a Python function implementing merge sort with type annotations", max_tokens=512)
print(output["choices"][0]["text"])

Hugging Face Hub:

from huggingface_hub import hf_hub_download
path = hf_hub_download(repo_id="FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF", filename="north-mini-code-1.0-mxfp4_moe.gguf")

Hardware

Quantized on NVIDIA GeForce RTX 5060 Ti (16 GB VRAM, Blackwell). Conversion time: ~13 minutes.

License

Apache-2.0 (same as original model).

Downloads last month: 669

GGUF

Model size

30B params

Architecture

cohere2moe

Hardware compatibility

4-bit

Model tree for FreedomAISVR/North-Mini-Code-1.0-MXFP4-GGUF

Base model

CohereLabs/North-Mini-Code-1.0

Quantized

(33)

this model