|
Download README.md from CQSystems/README: direct link, hf CLI and curl.
- Browser
- Download file 3.2 kB
-
https://huggingface.co/spaces/CQSystems/README/resolve/main/README.md
- Command line
-
hf download hf://spaces/CQSystems/README/README.md
-
curl -L -o README.md https://huggingface.co/spaces/CQSystems/README/resolve/main/README.md
3.2 kB
| title: README | |
| emoji: π¦ | |
| colorFrom: pink | |
| colorTo: purple | |
| sdk: static | |
| pinned: false | |
| license: mit | |
| thumbnail: >- | |
| https://cdn-uploads.huggingface.co/production/uploads/684e077edcb41cdac6183e2d/cFE0tIZqexfAFbsgnWq-U.png | |
| <div align="center"> | |
| <img src="cq_black.png"/> | |
| <h1>π CQ Systems</h1> | |
| <h3><i>To Support The Democratization of Foundational Intelligence</i></h3> | |
| <a href="https://www.cqurity.ai" target="_blank">www.cqurity.ai</a> | |
| </div> | |
| --- | |
| ## πΏ Our Mission: Sovereignty & Sustainability | |
| At **CQ Systems**, we believe that the future of intelligence must be decentralized. The current trajectory of AI development relies heavily on massive, energy-intensive data centers that centralize power and compromise privacy. We are working to break this cycle through two core pillars: | |
| ### π Ecological Responsibility | |
| By optimizing state-of-the-art models for consumer hardware, edge devices, and advanced silicon architectures, we aim to reduce the global reliance on hyper-scale data centers. Moving computation from massive server farms to local, efficient execution minimizes the carbon footprint of AI inference, promoting a more sustainable digital ecosystem. | |
| ### π Privacy & Assured Execution | |
| We are pushing privacy and assured execution back into the hands of the user. By enabling frontier-level models to run locally, we eliminate the need to transmit sensitive data to third-party clouds. This approach significantly reduces the regulatory hurdles associated with data retention and privacy compliance, empowering individuals and organizations to maintain absolute control over their own systems and information. | |
| **We aren't just optimizing weights; we are democratizing foundational intelligence.** | |
| --- | |
| ## π Our Work | |
| We specialize in highly optimized **GGUF quantizations** of top-tier foundation models. Our current focus is on the Google Gemma 4 family, providing a spectrum of models that balance extreme memory efficiency with uncompromising reasoning capabilities. | |
| ### π Model Repositories | |
| | Model Repository | Size / Type | Use Case | | |
| | :--- | :--- | :--- | | |
| | [**Google-Gemma-4-31B-It-GGUF**](https://huggingface.co/CQSystems/Google-Gemma-4-31B-It-GGUF) | 31B Dense | The heavyweight flagship; compressed for local execution without sacrificing frontier reasoning. | | |
| | [**CQ-Gemma-4-26B-A4B-It-GGUF**](https://huggingface.co/CQSystems/CQ-Gemma-4-26B-A4B-It-GGUF) | 26B (Active 4B) | A precision-engineered balance of depth and efficiency for high-performance local tasks. | | |
| | [**Google-Gemma-4-E4B-It-GGUF**](https://huggingface.co/CQSystems/Google-Gemma-4-E4B-It-GGUF) | 8B (4.5B Active) | *Our most popular release.* Optimized via iMatrix for Apple Silicon and edge devices. | | |
| | [**Google-Gemma-4-E2B-It-GGUF**](https://huggingface.co/CQSystems/Google-Gemma-4-E2B-It-GGUF) | 5B (2.3B Active) | Ultra-lightweight and lightning fast; purpose-built for constrained memory environments. | | |
| --- | |
| ## π€ Connect with Us | |
| * π **Website:** [cqurity.ai](https://www.cqurity.ai) | |
| * π‘ **Interests:** AI, Machine Learning, Quantization, Local LLMs, Model Optimization, Edge Computing | |
| *Building the future of local AI, one optimized tensor at a time.* |