The Church of Alvis is a guild founded in August of 2003 in Star Wars Galaxies. Since then we have spread to other games, but always maintained a connection with each other. With the release of Bioware and LucasArts’s Star Wars: The Old Republic we have reformed on the Juyo Canderous Ordo Jedi Covenant server. Please feel free to browse the site and let us know if you have any questions or are interested in joining.
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Check out the detailed setup guide below to begin.
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The PaddleOCR-VL-1.6-GGUF is a state‑of‑the‑art vision‑language model designed for high‑accuracy optical character recognition in multilingual documents. It leverages a transformer‑based encoder‑decoder architecture that jointly processes text and layout information, enabling robust recognition of curved and distorted scripts. The model supports over 100 languages and can handle a wide range of document types, from printed books to handwritten notes. Its quantized GGUF format ensures efficient inference on consumer‑grade hardware while maintaining competitive performance metrics. A built‑in language detection module automatically identifies the script, reducing preprocessing overhead. Users can integrate the model into existing pipelines via simple API calls, benefiting from its low memory footprint and fast loading times.
| Model Name | PaddleOCR-VL-1.6-GGUF |
| Architecture | Transformer‑based encoder‑decoder |
| Supported Languages | 100+ |
| Input Resolution | 1024×1024 pixels |
| Parameter Count | 1.6 B |
| Quantization | GGUF (Q4_K_M) |
| Hardware Requirements | CPU/GPU with ≥4 GB VRAM |
| License | Apache 2.0 |
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The fastest method for installing this model locally is by using Docker.
Make sure you implement the steps mentioned below.
Be patient as the system self-retrieves massive model weights dynamically.
You don’t need to tweak anything; the installer picks the highest performing setup.
OmniVoice is a next‑generation multimodal AI model that combines advanced speech recognition, natural language understanding, and high‑fidelity voice synthesis. It leverages transformer‑based architectures to process both audio and text streams in real time, enabling seamless interaction across diverse platforms. The model excels at contextual conversation, maintaining coherence across extended dialogues while adapting tone and style to match user preferences. Its integrated voice cloning capabilities allow for personalized audio output without compromising privacy or requiring extensive training data.
| Model Parameters | 12B |
| Inference Latency | <50 ms |
These technical highlights demonstrate OmniVoice’s superior performance and versatility in real‑world applications.
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The fastest tactical way to launch this model locally is via a Docker image.
Review and follow the instructions below.
Everything happens automatically, including the heavy cloud asset download.
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The Gemma-4-31B-it-qat-w4a16-ct is a large language model designed for instruction following and conversational tasks. It leverages 31 billion parameters to achieve a balance between accuracy and computational efficiency. The model employs QAT (quantized aware training) combined with a w4a16 format, enabling reduced memory footprint while preserving performance. Its CT architecture incorporates advanced attention mechanisms that improve context retention and response relevance. The following table summarizes key technical attributes.
| Parameter Count | 31 B |
| Quantization | QAT (w4a16) |
| Precision | 16‑bit float |
| Training Method | Instruction‑following fine‑tuning |
| Architecture | CT with enhanced attention |
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