How to use from
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"
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.

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GGUF
Model size
15B params
Architecture
qwen35
Hardware compatibility
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