aimeri/spoomplesmaxx-mockingbird-36B
<!doctype html> <html lang="en"> <head> <meta charset="UTF-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /> <title>SpoomplesMaxx Mockingbird 36B</title> </head> <style> @import url("https://fonts.googleapis.com/css2?family=Consolas&display=swap"); .crt-container { padding: 10px; max-width: 1000px; margin: 0 auto; width: 95%; } .crt-case { background: #e8d7c3; border-radius: 10px; padding: 15px; box-shadow: inset -2px -2px 5px rgba(0, 0, 0, 0.3), 2px 2px 5px rgba(0, 0, 0, 0.2); } .crt-inner-case { background: #e8d7c3; border-radius: 8px; padding: 3px; box-shadow: inset -1px -1px 4px rgba(0, 0, 0, 0.3), 1px 1px 4px rgba(0, 0, 0, 0.2); } .crt-bezel { background: linear-gradient(145deg, #1a1a1a, #2a2a2a); padding: 15px; border-radius: 5px; border: 3px solid #0a0a0a; position: relative; box-shadow: inset 0 0 20px rgba(0, 0, 0, 0.5), inset 0 0 4px rgba(0, 0, 0, 0.4), inset 2px 2px 4px rgba(255, 255, 255, 0.05), inset -2px -2px 4px rgba(0, 0, 0, 0.8), 0 0 2px rgba(0, 0, 0, 0.6), -1px -1px 4px rgba(255, 255, 255, 0.1), 1px 1px 4px rgba(0, 0, 0, 0.3); } .crt-bezel::before { content: ""; position: absolute; top: 0; left: 0; right: 0; bottom: 0; background: linear-gradient( 45deg, rgba(255, 255, 255, 0.03) 0%, rgba(255, 255, 255, 0) 40%, rgba(0, 0, 0, 0.1) 60%, rgba(0, 0, 0, 0.2) 100% ); border-radius: 3px; pointer-events: none; } .terminal-screen { background: #0c100d; padding: 20px; border-radius: 15px; position: relative; overflow: hidden; font-family: "Consolas", monospace; font-size: clamp(12px, 1.5vw, 16px); color: #3dc862; line-height: 1.4; text-shadow: 0 0 2px #3dc862; filter: brightness(1.1) contrast(1.1); box-shadow: inset 0 0 30px rgba(0, 0, 0, 0.9), inset 0 0 8px rgba(0, 0, 0, 0.8), 0 0 5px rgba(0, 0, 0, 0.6); max-width: 80ch; margin: 0 auto; } .terminal-screen h2, .terminal-screen h3 { font-size: clamp(16px, 2vw, 20px); margin-bottom: 1em; color: #ffdf00; text-shadow: 0 0 3px rgba(255, 223, 0, 0.5); } .terminal-screen pre.code-block-image { display: inline-block; text-align: left; font-size: clamp(2px, 0.4vw, 12px); font-family: monospace; margin: 1em 0; background-color: #1a1a1a; padding: 1em; border-radius: 4px; color: #3dc862; overflow-x: auto; line-height: 1; max-width: 100%; white-space: pre; } .terminal-screen pre.code-block { display: inline-block; text-align: left; font-size: clamp(10px, 1.3vw, 14px); font-family: monospace; margin: 1em 0; background-color: #1a1a1a; padding: 1em; border-radius: 4px; color: #3dc862; overflow-x: auto; line-height: 1; max-width: 100%; white-space: pre; } .terminal-screen::before { content: ""; position: absolute; top: 0; left: 0; right: 0; bottom: 0; background: linear-gradient( rgba(18, 16, 16, 0) 50%, rgba(0, 0, 0, 0.25) 50% ), url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADIAAAAyBAMAAADsEZWCAAAAGFBMVEUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4o8JoAAAAB3RSTlMAGwQIEQMYADcPzwAAACJJREFUKM9jYBgFo2AU0Beg+A8YMCLxGYZCbNQEo4BaAAD5TQiR5wU9vAAAAABJRU5ErkJggg=="); background-size: 100% 2.5px; pointer-events: none; z-index: 2; } .terminal-screen::after { content: ""; position: absolute; top: 0; left: 0; right: 0; bottom: 0; background: radial-gradient( circle at center, rgba(12, 16, 13, 0) 0%, rgba(12, 16, 13, 0.2) 50%, rgba(12, 16, 13, 0.15) 100% ); border-radius: 20px; pointer-events: none; z-index: 1; } .terminal-screen .notice { margin: 1.5em 0; padding: 0.8em 1.2em; border: 1px solid #ffdf00; border-radius: 4px; background-color: rgba(255, 223, 0, 0.04); } .terminal-screen .notice h3 { margin-top: 0.2em; margin-bottom: 0.5em; } .terminal-screen .notice p { margin-bottom: 0.2em; } .terminal-screen strong, .terminal-screen em { color: #f0f0f0; } .terminal-screen p, .terminal-screen li { color: #3dc862; } .terminal-screen a { color: #5da9ff; text-decoration: underline; text-shadow: 0 0 2px rgba(93, 169, 255, 0.5); transition: opacity 0.2s; } .terminal-screen a:hover { opacity: 0.8; } .terminal-screen code, .terminal-screen kbd, .terminal-screen samp { color: #3dc862; font-family: "Consolas", monospace; text-shadow: 0 0 2px #3dc862; background-color: #1a1a1a; padding: 0.2em 0.4em; border-radius: 4px; } </style> <div class="crt-container"> <div class="crt-case"> <div class="crt-inner-case"> <div class="crt-bezel"> <div class="terminal-screen"> <div style="text-align: center"> <h2>SpoomplesMaxx-Mockingbird-36B</h2> <h3>"Fat Mockingbird"</h3> <pre class="code-block-image"> ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░▒▒▒▒▒▒▒░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░▒▒▒▓▓▓▓▓▓▒▒░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▒▒▒▒░░░░░░░▒░░░░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▒▒▒▒▒▓▒▓▓▓▓▓▓▓▒▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▒▒▒▒▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▒▒▒▒▒▒▒▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░▒▒▒▒▒▓▒▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░▒▒▒▒▒▒▒▒▒▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▒▒▒▒▒▒▒▒▒▒▒▒▒▓▓▓▓▓▓▒▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▓▓▓▓▓▓░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒░▓▓▓▓▓▓░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░▒▒▒▒▒░▒▒▒░░░░░░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▒░░░▒░░░░░░░▓▓▓▓▓▓▒▓▓▓▓▓▓▒▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░░░░░░░░░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░▓░▓░▒░░░░░░░░░▓▓▓▓▓▓▓▓░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▒░░░▓░░░░░░▓▓▓▓▓▓▒▒▓▓▓▓▓▓▓▓░▓▓▓▓▓▓▓▓▓▓▓▒░░▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░▓░░░░▓░░░░░▒▓░░▓▓▓▓▓▓▓▓▓▓░▓▓▓▓▓▓▓▓▓▓░░░░░▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▒░░░▓░░▓░░░░░░░▒▒▓▓▓▓▓▓▓▓▓░▓▓▓▓▓▓▓▓▓▓▓░░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░▓░░░░░░░░░▒▒▓▓▓▓▓▓▓▓▓▓▓░▓▓▓▓▓▓▓▓▓▓▓░░░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░░░▒▒▒▒▒▓▓▓▓▓▓▓▓░▓▓▓▓▓▓▓▓▓▓▓▓▓▒░░░░▒▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░▒▒▒▒▒▒▒▒▒▒▒▒▒░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░▓▓░▓▓▓▒░░▓▓▓▓▓░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░▓▓▓▓░░░░░░░▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓░░▓▓▓▓▓▓▓▓▓▓▓▓▓░▒░░░▒░▓░▓▓▓▓▓▓▓▓░▓▓▓▓▓▓▓░▓▓▓▓▓▓▓▓▓▓▓▓░░▓░░░░░░░░░░░░░▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓░░░░▓▓▓▓▓▓▓▓▓▓▓▓░░░░▓▓▓▓▓▓▓▓▓▓▓▓▒░▓▓▓▓▓▓▓▓░▓▓▓▓▓▓░░░░░░░▓▓▓▓▓░░░░░░░░▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▒░░░░▓▓▓▓▓▓▓▓▓░░░▓░░▓▓▓▓▓▓▓▓▓▓▓▓▓░▓▓▓▓▓▓▓▓▓░▒░░░░░░▓▓▓▓▓░░░░▒▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓░░░░▓▓▓▓▓▓▓▓░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░▓▓▓▓▓▓░░░░░░▓▓▓▓▓▓▓▓▓▓░░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓░░░▓▓▓▓▓▓░░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░░░▓░░▓▓▓▓▓▓▓▓▓▓▓░░░░░▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓░▓▓▓▓▓░░░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░▓░░▒▓▓▓░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▒░░░░░░░▓▓▓▓▓░░░▒░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░▒▒▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓░░░░░▒▓░░▓░░░▒░▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░▓▓▓▓░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░▒▓▓▓▓▓▓▓▓▓░░░░░░▒▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░▓▓▓▓▓▓▓░░░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░▓▓▓▓▓▓▓░░░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓░░░░░░▓░░▓▓▒░░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓░░░▒░░░▓░░░░░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓░░▓▓▓░░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ </pre> </div> <p> <strong>"Many-Tongued"</strong> — <em>Mimus polyglottos</em>, the many-tongued mimic. A bird with no song of its own and therefore all of them: it will do the cardinal, the car alarm, the creaky gate, and a frog if it hears one. First of the mimids, the family that follows the corvids. </p> <p> The corvids (jackdaw, magpie, whiskeyjack) were generalists with a roleplay bent. mockingbird flips the recipe: <strong>a model that is 100% about roleplay, trained mostly on things that are not roleplay.</strong> That is not a contradiction — it is the finding. Measured across the strongest open RP lineage I know (<a href="https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-24b">Dans-PersonalityEngine</a>), roughly 590K of its rows are task/reasoning/assistant/world-knowledge data against ~150K of actual roleplay. RP is the product; RP is not the corpus. The RP data teaches the register. Everything else teaches the mind behind it. </p> <p> Built on <strong>Seed-OSS-36B-Base-woSyn</strong> — the base ByteDance trained <em>without</em> synthetic instruction data. The wildest 36B available: nobody else's assistant habits, nobody else's turn-taking tics. A blank throat, ready to mimic. </p> <h3>Who this is for</h3> <p> 36B dense is a lot of model, and no friend to the VRAM-challenged — something I'm genuinely sorry about. But the target here was the best-quality RP under 70B, and every choice in this card spends toward that target. It won't be for everyone, and it doesn't have to be: whiskeyjack exists for exactly that reason. The mimids are an experiment in RP quality, exclusively. The 3-bit quants (~18GB) are as small as this one gets. </p> <h3>Prompt format</h3> <p> Native Seed convention. No new tokens were harmed in the making of this model. </p> <pre class="code-block"> FORM <seed:bos>system\n{card}<seed:eos><seed:bos>user\n{text}<seed:eos><seed:bos>assistant\n{reply}<seed:eos> STOPS <seed:eos> ROLES system / user / assistant
EXAMPLE <seed:bos>system You are Bram Hollis, keeper of the Wayward Lantern...<seed:eos><seed:bos>user I push the door open, dripping wet Got room for one more?<seed:eos><seed:bos>assistant </pre> <p> The template ships embedded (<code>chattemplate.jinja</code> + <code>tokenizerconfig.json</code>), so vLLM, llama.cpp, and the quants pick it up without ceremony. Anything that can serve Seed-OSS-Instruct serves mockingbird. </p> <div class="notice"> <h3>No thinking. Ever.</h3> <p> mockingbird never emits <code><seed:think></code> and was never trained on reasoning traces </p> </div> <h3>Tool calling</h3> <p> The corpus includes the full Toolmaxx family (58,095 conversations), rendered with tool responses as a plain <code>tool</code> role turn: </p> <pre class="code-block"> <seed:bos>tool\n{tool output}<seed:eos> </pre> <div class="notice"> <h3>Not the Seed-OSS-Instruct tool DSL.</h3> <p> No <code><seed:toolcall></code> tokens, no <code><function=...></code> markup. Tool competence here is corpus-taught and conversational, not a structured calling API. If you need strict function calling, put a schema in the card and validate what comes back </p> </div> <h3>Key details</h3> <pre class="code-block"> BASE ByteDance-Seed/Seed-OSS-36B-Base-woSyn (Apache 2.0) PARAMS 36B dense · 64 layers · GQA 8 KV heads · headdim 128 VOCAB 155,136 · native Seed control tokens · zero added tokens CTX trained at 24,576 packed · base RoPE to 512K CORPUS 667,332 conversations · ~1.3B supervised chars · 43% RP share THINKING none, by construction LANGUAGE English (non-English filtered at ingest) </pre> <h3>Training</h3> <p> Full-parameter SFT, <a href="https://github.com/axolotl-ai-cloud/axolotl">Axolotl</a>, 8×B200. One stage, no annealing games: </p> <pre class="code-block"> STEPS 584 run of 910 planned (funding cliff) · this release = step 450 SEQ 24,576 · sample packing (99.94% efficiency) BATCH 64 global (micro 1 × accum 8 × 8 GPUs) OPT AdamW · lr 8e-6 cosine · 3% warmup · wd 0.01 · bf16 STACK FSDP2 full-shard · activation checkpointing · Cut Cross Entropy
VAL PPL base 4.272 → step 100: 4.094 (min) → step 550: 4.179 </pre> <p> Val perplexity bottoms out early and drifts up; it did not pick this checkpoint. Selection ran the other way: every 50th checkpoint through a seeded multi-turn loop/stall battery (×5 repeats), a 20-arm sampler sweep across the finalists (400/450/500) at 16K context, and blind-judged episodes on real character cards. Step 450 won on both instruments: the highest battery pass rate in the whole sweep matrix at its shipped sampler, and the most coherent judged episodes. Earlier checkpoints still loop; later ones need temperatures where coherence frays. </p> <p> The corpus is the PersonalityEngine V1.3.0 public list — all 42 non-gated sets, ingested verbatim — plus my own lanes on top: 16,714 carded RP conversations (anthracite c2, Gryphe Aesir, PJMixers, bluemoon), 8,872 think-stripped RP logs, and a 385-conversation anti-repetition lane built to reconstruct the gated RepRemover idea: find the turn that repeats an earlier turn, cut there, rewrite the continuation to advance the scene, accept only if it clears a Jaccard 0.35 gate against every prior turn. </p> <div class="notice"> <h3>The corpus was cleaned so the model doesn't have to be.</h3> <p> Dropped at ingest, with receipts: <code>Name:</code>-style fiction-transcript openers (up to 18.2% of one source — the classic RP defect), mid-scene policy refusals and jailbreak-compliance preambles (851 + 452 conversations - not really needed for this model), non-English rows, exact duplicates across lanes (5,332). Conversations longer than the context window were split at turn boundaries with the card re-carried, not truncated (one Personamaxx-VN row was a single 4.85M-char turn; it did not make the cut). </p> </div> <h3>Sampling</h3> <p> The shipped <code>generationconfig.json</code> is the measured optimum, not a guess — it won a 20-arm sweep (temperature × topp × minp × penalties, ×5 seeded battery runs per arm, plus blind-judged episodes): </p> <pre class="code-block"> temperature 1.0 · topp 0.9 · no penalties </pre> <p> The usable window is narrow and hotter than RP muscle memory expects: <strong>~0.95–1.05</strong>. Below ~0.85 the model collapses into verbatim self-repetition (at 0.7 it re-emits its previous turn nearly word for word). Above ~1.15 turn-endings slip and the prose goes dreamlike. minp alone (0.05, topp off) truncates harder than top_p 0.9 and loops <em>more</em>, not less. This is not a temperature-0.7 model. </p> <div class="notice"> <h3>Never use repetition, presence, or frequency penalties.</h3> <p> The Seed template ends every message with <code><seed:eos></code>, so a multi-turn chat has dozens of them in context. Context-wide penalties tax that token directly: the model stops being able to end its turn, the penalty then strip-mines the English vocabulary, and generation falls into the base model's untrained Chinese tokens (<code>爹爹爹爹爹…</code> — it is exactly as bad as it looks). If you need anti-repetition, use DRY or XTC, which leave special tokens alone. The corpus's anti-repetition lane plus temperature 1.0 is the intended mechanism. </p> </div> <p> Give it a proper card and it will give you a proper character: the model was fed real character cards (median ~3K chars, p90 ~8.5K) as system messages. </p> <h3>Quickstart</h3> <pre class="code-block"> from transformers import AutoModelForCausalLM, AutoTokenizer
modelid = "aimeri/spoomplesmaxx-mockingbird-36B" tok = AutoTokenizer.frompretrained(modelid) model = AutoModelForCausalLM.frompretrained( modelid, torchdtype="bfloat16", device_map="auto")
messages = [ {"role": "system", "content": "You are Bram Hollis, keeper of the " "Wayward Lantern, a roadside inn on the edge of the fen. Gruff, " "observant, superstitious. Third person, asterisk action beats."}, {"role": "user", "content": "I push the door open, dripping wet " "Got room for one more tonight?"}, ] ids = tok.applychattemplate(messages, addgenerationprompt=True, returntensors="pt").to(model.device) out = model.generate(ids, maxnewtokens=400, temperature=1.0, topp=0.9, dosample=True) print(tok.decode(out[0][ids.shape[-1]:], skipspecialtokens=True)) </pre> <p>Quants: <a href="https://huggingface.co/aimeri/spoomplesmaxx-mockingbird-36B-mlx-3bit">MLX 3-bit</a> (15GB, Apple silicon) · <a href="https://huggingface.co/aimeri/spoomplesmaxx-mockingbird-36B-GGUF">GGUF static Q3–Q5</a> · <a href="https://huggingface.co/aimeri/spoomplesmaxx-mockingbird-36B-i1-GGUF">GGUF imatrix</a> (weighted on the model’s own corpus — prefer these at 3–4 bit; i1-Q3KM is the 18GB target, i1-IQ3XXS squeezes to 14GB)</p> <p>This one already knows your character better than you do.</p> <p> <em>mockingbird is a roleplay and creative-writing model for adults. It stays in character by design — its corpus was scrubbed of mid-scene refusals — so bring your own moderation where your deployment needs it. Not an assistant, not an oracle, not for anything safety-critical.</em> </p> <p> <em>mimids 01 · trained 2026-08 · checkpoints published live at <a href="https://huggingface.co/aimeri/mockingbird-v1-seedoss-ckpts">mockingbird-v1-seedoss-ckpts</a> · Apache 2.0</em> </p> </div> </div> </div> </div> </div>
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