Instructions to use infosave/cortiq-coder-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use infosave/cortiq-coder-12B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="infosave/cortiq-coder-12B",
	filename="cortiq-coder-12b-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 infosave/cortiq-coder-12B 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 infosave/cortiq-coder-12B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf infosave/cortiq-coder-12B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf infosave/cortiq-coder-12B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf infosave/cortiq-coder-12B: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 infosave/cortiq-coder-12B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf infosave/cortiq-coder-12B: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 infosave/cortiq-coder-12B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf infosave/cortiq-coder-12B:Q4_K_M

Use Docker

docker model run hf.co/infosave/cortiq-coder-12B:Q4_K_M

LM Studio
Jan
Ollama
How to use infosave/cortiq-coder-12B with Ollama:
```
ollama run hf.co/infosave/cortiq-coder-12B:Q4_K_M
```

Unsloth Studio

How to use infosave/cortiq-coder-12B 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 infosave/cortiq-coder-12B 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 infosave/cortiq-coder-12B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for infosave/cortiq-coder-12B to start chatting

How to use infosave/cortiq-coder-12B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf infosave/cortiq-coder-12B: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": "infosave/cortiq-coder-12B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use infosave/cortiq-coder-12B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf infosave/cortiq-coder-12B: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 infosave/cortiq-coder-12B:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use infosave/cortiq-coder-12B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf infosave/cortiq-coder-12B: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 "infosave/cortiq-coder-12B: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 infosave/cortiq-coder-12B with Docker Model Runner:
```
docker model run hf.co/infosave/cortiq-coder-12B:Q4_K_M
```

Lemonade

How to use infosave/cortiq-coder-12B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull infosave/cortiq-coder-12B:Q4_K_M

Run and chat with the model

lemonade run user.cortiq-coder-12B-Q4_K_M

List all available models

lemonade list

cortiq-coder-12B / README.md

infosave

Update README.md

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metadata

license: apache-2.0
library_name: gguf
tags:
  - coding
  - code-generation
  - qwen3
  - gguf
  - cortiq
language:
  - en
base_model: Qwen/Qwen3-27B

Cortiq Coder 12B

Cortiq Coder 12B is a task-specialized coding model compiled from Qwen3-27B down to ~12B effective parameters using a proprietary dynamic neural network compression method developed by AllAIGate.

The compression is performed via the CORTIQ method — a system and method for Dynamic Task-Guided Neural Network Compression with Catastrophic Forgetting Prevention, covered under US Patent Application No. 19/452,464 (filed January 19, 2026).

Unlike naive pruning or quantization, CORTIQ preserves task-critical knowledge during compression by dynamically guiding the pruning process toward the target domain (code generation), while actively preventing degradation of the model's core reasoning capabilities.

Key Features

🔧 Optimized for code generation — structured compression guided by coding tasks
🧠 Based on Qwen3-27B — retains strong reasoning foundation of the 27B base model at 12B scale
🚀 GGUF Q4_K_M quantization — ready for efficient local inference
🛡️ Catastrophic forgetting prevention — task-specific compression without degrading general capabilities
📦 Compact footprint — 9.37 GB in GGUF Q4_K_M format

Files

File	Format	Size	Description
`cortiq-coder-12b-Q4_K_M.gguf`	GGUF	9.37 GB	Quantized model for llama.cpp / LM Studio / Ollama
`cortiq-coder-12b-nvg.tar`	TAR	30.1 GB	Full native model weights

Quick Start

llama.cpp

llama-server -hf infosave/cortiq-coder-12B:Q4_K_M

Ollama

ollama run hf.co/infosave/cortiq-coder-12B:Q4_K_M

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="infosave/cortiq-coder-12B",
    filename="cortiq-coder-12b-Q4_K_M.gguf",
)
response = llm.create_chat_completion(messages=[
    {"role": "user", "content": "Write a Python function to sort a list of dicts by key."}
])
print(response["choices"]["message"]["content"])

Method Reference

Patent: US Application No. 19/452,464 — "System and Method for Dynamic Task-Guided Neural Network Compression with Catastrophic Forgetting Prevention" — Filed January 19, 2026.
Details: https://allaigate.com/ru/

License

Released under the Apache 2.0 License, consistent with the Qwen3 base model license.