Instructions to use ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF",
	filename="North-Mini-Code-1.0-ik_llama-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 Settings

llama.cpp

How to use ji-farthing/North-Mini-Code-1.0-ik-llama-validation-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 ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ji-farthing/North-Mini-Code-1.0-ik-llama-validation-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 ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ji-farthing/North-Mini-Code-1.0-ik-llama-validation-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 ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ji-farthing/North-Mini-Code-1.0-ik-llama-validation-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 ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF with Ollama:
```
ollama run hf.co/ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF:Q4_K_M
```

Unsloth Studio

How to use ji-farthing/North-Mini-Code-1.0-ik-llama-validation-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 ji-farthing/North-Mini-Code-1.0-ik-llama-validation-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 ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ji-farthing/North-Mini-Code-1.0-ik-llama-validation-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": "ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ji-farthing/North-Mini-Code-1.0-ik-llama-validation-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 ji-farthing/North-Mini-Code-1.0-ik-llama-validation-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 ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ji-farthing/North-Mini-Code-1.0-ik-llama-validation-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 "ji-farthing/North-Mini-Code-1.0-ik-llama-validation-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 ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF with Docker Model Runner:
```
docker model run hf.co/ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF:Q4_K_M
```

Lemonade

How to use ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.North-Mini-Code-1.0-ik-llama-validation-GGUF-Q4_K_M

List all available models

lemonade list

North-Mini-Code-1.0 GGUFs converted with ik_llama.cpp

These GGUF files are reviewer-facing and community-usable artifacts for CohereLabs North-Mini-Code-1.0 support in ik_llama.cpp.

They were converted from the official CohereLabs North-Mini-Code-1.0 checkpoint with the ik_llama.cpp Cohere2-MoE support added in PR #1945.

No performance or quality claims are made for these files. Local validation only confirms that the listed GGUFs load and respond on the tested hardware/runtime.

Files

File	Quant	Size	SHA256
`North-Mini-Code-1.0-ik_llama-Q8_0.gguf`	Q8_0	33,007,688,960 bytes	`2e8139305d30f31ed7a5834c32113f9c6ce5d004bf0ec9008be6da0a20928a50`
`North-Mini-Code-1.0-ik_llama-Q6_K.gguf`	Q6_K	25,522,707,712 bytes	`8661540adc05ccba8cd90e36ca0f29101586a7e201090bc503db1ca11cb4d37d`
`North-Mini-Code-1.0-ik_llama-Q4_K_M.gguf`	Q4_K_M	18,991,947,008 bytes	`0dfed0306ef9e0a7887bac556494e4e36b65b116c48976cb0d8283fd48a006cd`

Source And Build Notes

Source model: CohereLabs/North-Mini-Code-1.0
Source revision used locally: effaeda477c041c107d5a3d8c599cb5d6c5878ef
Converter/runtime support: ik_llama.cpp main after PR #1945, or an equivalent build with cohere2_moe support
Build commit used for these artifacts: 1e063a6bd Enhance Cohere2-MoE support by modifying tensor handling and configuration logic
Conversion intermediate: BF16 GGUF, then quantized with llama-quantize
Embedded chat template: source chat_template.jinja
Embedded chat template SHA256 prefix: d8366efb9f07c571
Tokenizer pre-tokenizer marker: tokenizer.ggml.pre=cohere2_moe

Relevant metadata present in all listed GGUFs:

general.architecture=cohere2_moe
tokenizer.ggml.pre=cohere2_moe
tokenizer.chat_template is embedded and matches the source chat_template.jinja

Validation

Validation was run locally on an RTX 4070 workstation with an ik_llama.cpp build containing Cohere2-MoE / North-Mini-Code support.

Checks performed:

BF16 conversion completed successfully.
Q8_0, Q6_K, and Q4_K_M quantization completed successfully.
Metadata was inspected after quantization.
The embedded chat template in Q8_0, Q6_K, and Q4_K_M exactly matched the source chat_template.jinja.
Q8_0, Q6_K, and Q4_K_M loaded through llama-server with the Cohere2-MoE runtime path.
Q8_0, Q6_K, and Q4_K_M passed a short OpenAI-compatible chat sanity check.
Q8_0, Q6_K, and Q4_K_M were also exercised through a local OpenCode configuration against an OpenAI-compatible endpoint and produced code in that harness.

These checks are compatibility smokes only. They are not benchmark results and should not be interpreted as performance, quality, or agentic coding claims.

Compatibility

These files require ik_llama.cpp main after PR #1945, or another runtime with equivalent cohere2_moe architecture, tensor-loading, tokenizer, and graph support.

They are not expected to load or decode correctly in runtimes that do not yet understand general.architecture=cohere2_moe.

Downloads last month: 567

GGUF

Model size

31B params

Architecture

cohere2_moe

Hardware compatibility

4-bit

6-bit

8-bit

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Model tree for ji-farthing/North-Mini-Code-1.0-ik-llama-validation-GGUF

Base model

CohereLabs/North-Mini-Code-1.0

Quantized

(35)

this model