How to use from
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
Quick Links

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

  • ๐Ÿ”ง Optimized for code generation โ€” structured compression guided by coding tasks
  • ๐Ÿง  Based on Qwen3-27B โ€” retains strong reasoning foundation of the 27B base model at 12B scale
  • ๐Ÿš€ GGUF Q4_K_M quantization โ€” ready for efficient local inference
  • ๐Ÿ›ก๏ธ Catastrophic forgetting prevention โ€” task-specific compression without degrading general capabilities
  • ๐Ÿ“ฆ 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.

Downloads last month
6
GGUF
Model size
15B params
Architecture
qwen35
Hardware compatibility
Log In to add your hardware

4-bit

Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐Ÿ™‹ Ask for provider support