Instructions to use infosave/cortiq-coder-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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 from brew
brew install llama.cpp # 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
Install from WinGet (Windows)
winget install llama.cpp # 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
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
- Pi
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-server -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-server -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
- 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
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 โ 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
- ๐ง Optimized for code generation โ structured compression guided by coding tasks
- ๐ง Based on Qwen3-27B โ retains strong reasoning foundation of the 27B base model at 12B scale
- ๐ GGUF Q4_K_M quantization โ ready for efficient local inference
- ๐ก๏ธ Catastrophic forgetting prevention โ task-specific compression without degrading general capabilities
- ๐ฆ Compact footprint โ 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 โ "System and Method for Dynamic Task-Guided Neural Network Compression with Catastrophic Forgetting Prevention" โ Filed January 19, 2026.
Details: https://allaigate.com/ru/
License
Released under the Apache 2.0 License, consistent with the Qwen3 base model license.
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