infosave/cortiq_qwopus_dev

license: mit ---

Cortiqqwopusdev

Cortiq_qwopus_dev 12B is a task-specialized coding model compiled from Jackrong/Qwopus3.6-27B-v2-MTP-GGUF, a Multi-Token Prediction (MTP) reasoning model ultimately derived from Qwen3.6-27B.

The original 27B model is compressed down to an effective ~12B 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 pure quantization, CORTIQ preserves task‑critical knowledge during compression by dynamically guiding the pruning process toward the target domain (code generation / agentic coding), while actively preventing degradation of the model's core reasoning capabilities.

Model Details

Note: “12B” refers to the effective parameter budget of the compressed topology; Hugging Face reports ~15B stored BF16 parameters for this checkpoint.

Why Qwopus3.6-27B-v2-MTP as Base?

Qwopus3.6-27B-v2-MTP is a reasoning‑centric variant of Qwen3.6‑27B with Multi‑Token Prediction and dedicated alignment for reasoning, coding, DevOps, and math. It already incorporates:

• —MTP speculative decoding for higher throughput on long sequences
• —Training focused on structured reasoning and code / math workflows
• —A Qwen3.6‑27B backbone with strong general‑purpose capabilities

Cortiqqwopusdev inherits these strengths and then further specializes them via CORTIQ toward coding + agentic / tool‑use scenarios.

CORTIQ Compression

CORTIQ is a dynamic, task‑guided compression pipeline designed to retain reasoning and coding ability under strong parameter reduction:

1. 1.Task‑guided pruning – importance is measured under code‑centric workloads; pruning focuses on preserving coding and reasoning subspaces.
2. 2.Catastrophic forgetting prevention – regularization and replay prevent collapse of instruction‑following and general reasoning during compression.
3. 3.Layer‑wise adaptation – pruning ratios differ per layer/head based on activation statistics instead of a uniform global threshold.

The result is a ~12B‑effective model with significantly lower memory and better latency compared to the original 27B model, while keeping most of its coding and reasoning performance.

Intended Use

Cortiqqwopusdev is designed primarily for agentic coding workflows:

• —Code generation (functions, classes, modules) from natural‑language specs
• —Code completion and in‑editor assistance
• —Debugging and error analysis (explain exceptions, suggest fixes)
• —DevOps / infra automation (scripts, configs, runbooks)
• —Code explanation for education / documentation
• —Tool‑use / function calling in coding agents

Target stacks include (but are not limited to): Python, JavaScript/TypeScript, C/C++, Rust, Go, Java, SQL, Bash, and infrastructure‑as‑code ecosystems.

Usage

llama.cpp

Instructions below come from the Hugging Face “local apps” integration for infosave/cortiq_qwopus_dev [page:1].

# Install via Homebrew (macOS / Linux)
brew install llama.cpp

# Start a local OpenAI-compatible server with web UI:
llama-server -hf infosave/cortiq_qwopus_dev:Q4_K_M

# Run inference directly in the terminal:
llama-cli -hf infosave/cortiq_qwopus_dev:Q4_K_M

Windows (WinGet):

winget install llama.cpp

# Server:
llama-server -hf infosave/cortiq_qwopus_dev:Q4_K_M

# CLI:
llama-cli -hf infosave/cortiq_qwopus_dev:Q4_K_M

Prebuilt binary (GitHub releases of llama.cpp):

./llama-server -hf infosave/cortiq_qwopus_dev:Q4_K_M
./llama-cli    -hf infosave/cortiq_qwopus_dev:Q4_K_M

Python (llama-cpp-python)

Сниппет также берётся напрямую из страницы модели [page:1]:

# pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="infosave/cortiq_qwopus_dev",
    filename="qwopus-nvg-12b-F16.gguf",
)

resp = llm.create_chat_completion(
    messages=[
        {"role": "user", "content": "Write a Python quicksort implementation."}
    ]
)

print(resp["choices"]["message"]["content"])

Ollama

ollama run hf.co/infosave/cortiq_qwopus_dev:Q4_K_M

LM Studio / Jan / Unsloth / другие клиенты

Модель уже интегрирована в стандартные “local apps” Hugging Face (LLM Studio, Jan, Unsloth, Pi, Hermes Agent, Docker Model Runner, Lemonade и др.), и может быть выбрана поиском по имени infosave/cortiq_qwopus_dev [page:1].

Limitations

• —Модель специализирована под код и агентные сценарии; для чисто “общечатовых” задач необязательно будет оптимальна.
• —Крайне длинный контекст с множеством файлов и инструкций может ухудшать качество генерации.
• —Не предназначена для формально верифицированной или safety‑critical разработки; всегда проверяйте вывод перед использованием в проде.

License

This model is released under the MIT License (as specified on the model page). [page:1]

The underlying CORTIQ compression method is proprietary and patent‑pending. Commercial use of the weights follows MIT; separate licensing may be required for direct use of the CORTIQ pipeline itself.

Citation

@misc{allaigate2026cortiq_qwopus_dev,
  title        = {Cortiq\_qwopus\_dev 12B:
                  Task-Specialized Coding via Dynamic Compression
                  from Qwopus3.6-27B-v2-MTP},
  author       = {AllAIGate},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/infosave/cortiq_qwopus_dev}},
  note         = {Base: Jackrong/Qwopus3.6-27B-v2-MTP-GGUF.
                  CORTIQ method: US Patent Application No. 19/452,464}
}