README.md

---
license: mit
language:
- en
base_model:
- Jackrong/Qwopus3.6-27B-v2-MTP-GGUF
---
---
license: mit
---

# Cortiq_qwopus_dev

**Cortiq_qwopus_dev 12B** is a task-specialized coding model compiled from  
[Jackrong/Qwopus3.6-27B-v2-MTP-GGUF](https://huggingface.co/Jackrong/Qwopus3.6-27B-v2-MTP-GGUF),  
a Multi-Token Prediction (MTP) reasoning model ultimately derived from  
[Qwen3.6-27B](https://huggingface.co/Qwen/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](https://allaigate.com/).

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

| Property            | Value                                                          |
|---------------------|----------------------------------------------------------------|
| **Repository**      | `infosave/cortiq_qwopus_dev`                                   |
| **Format(s)**       | Safetensors, GGUF                                              |
| **GGUF filename**   | `qwopus-nvg-12b-F16.gguf`                                      |
| **Base model**      | [Qwopus3.6-27B-v2-MTP-GGUF](https://huggingface.co/Jackrong/Qwopus3.6-27B-v2-MTP-GGUF) |
| **Base root**       | Qwen3.6-27B                                                    |
| **Architecture**    | `qwen3_5_text` (decoder-only transformer)                      |
| **Model size**      | ~15B stored params (BF16)                                      |
| **Effective size**  | ~12B parameters after CORTIQ compression                       |
| **Tensor type**     | BF16                                                           |
| **License**         | MIT                                                            |
| **Compression**     | CORTIQ (Dynamic Task-Guided Compression + CF prevention)       |
| **Developer**       | [AllAIGate](https://allaigate.com/)                            |

*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  

Cortiq_qwopus_dev 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. **Task‑guided pruning** – importance is measured under code‑centric
   workloads; pruning focuses on preserving coding and reasoning subspaces.
2. **Catastrophic forgetting prevention** – regularization and replay prevent
   collapse of instruction‑following and general reasoning during compression.
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

Cortiq_qwopus_dev 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].

```bash
# 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):

```bash
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):

```bash
./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]:

```python
# 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

```bash
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

```bibtex
@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}
}
```