- { box-sizing: border-box; margin: 0; padding: 0; }
body { background: var(–bg); color: var(–text); font-family: ‘Syne’, sans-serif; min-height: 100vh; padding: 0 0 80px; position: relative; overflow-x: hidden; }
/* Animated grid background */ body::before { content: ‘’; position: fixed; inset: 0; background-image: linear-gradient(rgba(123,94,167,0.04) 1px, transparent 1px), linear-gradient(90deg, rgba(123,94,167,0.04) 1px, transparent 1px); background-size: 48px 48px; pointer-events: none; z-index: 0; }
/* Glowing orbs */ .orb { position: fixed; border-radius: 50%; filter: blur(100px); pointer-events: none; z-index: 0; opacity: 0.18; animation: drift 12s ease-in-out infinite alternate; } .orb1 { width: 500px; height: 500px; background: var(–accent); top: -100px; left: -100px; } .orb2 { width: 400px; height: 400px; background: var(–accent2); bottom: 0; right: -80px; animation-delay: -6s; } .orb3 { width: 300px; height: 300px; background: var(–accent3); top: 50%; left: 40%; animation-delay: -3s; }
@keyframes drift { from { transform: translate(0, 0) scale(1); } to { transform: translate(30px, 40px) scale(1.1); } }
.page { max-width: 860px; margin: 0 auto; padding: 0 24px; position: relative; z-index: 1; }
/* ── HERO ── */ .hero { padding: 72px 0 48px; border-bottom: 1px solid var(–border); } .hero-eyebrow { font-family: ‘Space Mono’, monospace; font-size: 11px; letter-spacing: 0.2em; text-transform: uppercase; color: var(–accent2); margin-bottom: 20px; display: flex; align-items: center; gap: 10px; } .hero-eyebrow::after { content: ‘’; height: 1px; width: 60px; background: var(–accent2); opacity: 0.5; } .hero-title { font-size: clamp(22px, 4vw, 36px); font-weight: 800; line-height: 1.15; letter-spacing: -0.02em; margin-bottom: 16px; } .hero-title .accent { color: var(–accent); } .hero-title .accent2 { color: var(–accent2); } .hero-org { font-family: ‘Space Mono’, monospace; font-size: 13px; color: var(–muted); margin-bottom: 28px; } .hero-org a { color: var(–accent2); text-decoration: none; } .hero-org a:hover { text-decoration: underline; }
.tag-row { display: flex; flex-wrap: wrap; gap: 8px; margin-bottom: 32px; } .tag { font-family: ‘Space Mono’, monospace; font-size: 11px; background: var(–tag-bg); border: 1px solid var(–border); color: var(–muted); padding: 4px 10px; border-radius: 4px; letter-spacing: 0.04em; } .tag.hot { color: var(–accent3); border-color: rgba(232,93,117,0.3); } .tag.blue { color: var(–accent2); border-color: rgba(62,207,207,0.3); } .tag.purple { color: var(–accent); border-color: rgba(123,94,167,0.4); }
.stat-row { display: grid; grid-template-columns: repeat(auto-fit, minmax(140px, 1fr)); gap: 16px; padding-top: 8px; } .stat-card { background: var(–surface); border: 1px solid var(–border); border-radius: 10px; padding: 16px 20px; position: relative; overflow: hidden; } .stat-card::before { content: ‘’; position: absolute; top: 0; left: 0; right: 0; height: 2px; background: linear-gradient(90deg, var(–accent), var(–accent2)); } .stat-label { font-size: 10px; font-family: ‘Space Mono’, monospace; text-transform: uppercase; letter-spacing: 0.1em; color: var(–muted); margin-bottom: 6px; } .stat-value { font-size: 20px; font-weight: 700; color: var(–text); } .stat-value .unit { font-size: 13px; font-weight: 400; color: var(–muted); }
/* ── SECTIONS ── */ .section { padding: 48px 0 0; } .section-header { display: flex; align-items: center; gap: 14px; margin-bottom: 24px; } .section-num { font-family: ‘Space Mono’, monospace; font-size: 11px; color: var(–accent); opacity: 0.6; } .section-title { font-size: 20px; font-weight: 700; letter-spacing: -0.01em; } .section-title span { color: var(–accent2); } .section-line { flex: 1; height: 1px; background: var(–border); }
p { font-size: 15px; line-height: 1.75; color: #c5c5df; margin-bottom: 16px; } p:last-child { margin-bottom: 0; }
/* ── DESCRIPTION BOX ── */ .desc-box { background: var(–surface); border: 1px solid var(–border); border-left: 3px solid var(–accent); border-radius: 10px; padding: 24px 28px; }
/* ── DATASET TABLE ── */ .dataset-grid { display: grid; gap: 10px; } .dataset-item { display: flex; align-items: center; gap: 14px; background: var(–surface); border: 1px solid var(–border); border-radius: 8px; padding: 14px 18px; transition: border-color 0.2s, background 0.2s; } .dataset-item:hover { border-color: rgba(123,94,167,0.5); background: #14141f; } .dataset-icon { width: 34px; height: 34px; border-radius: 6px; background: linear-gradient(135deg, var(–accent), var(–accent2)); display: flex; align-items: center; justify-content: center; font-size: 14px; flex-shrink: 0; } .dataset-name { font-family: ‘Space Mono’, monospace; font-size: 12px; color: var(–accent2); word-break: break-all; } .dataset-name a { color: inherit; text-decoration: none; } .dataset-name a:hover { text-decoration: underline; } .dataset-type { margin-left: auto; font-size: 10px; font-family: ‘Space Mono’, monospace; text-transform: uppercase; color: var(–muted); letter-spacing: 0.08em; flex-shrink: 0; }
/* ── TRAINING GRID ── */ .training-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(220px, 1fr)); gap: 14px; } .param-card { background: var(–surface); border: 1px solid var(–border); border-radius: 10px; padding: 18px 20px; } .param-key { font-family: ‘Space Mono’, monospace; font-size: 10px; text-transform: uppercase; letter-spacing: 0.1em; color: var(–muted); margin-bottom: 8px; } .param-val { font-size: 16px; font-weight: 700; color: var(–text); font-family: ‘Space Mono’, monospace; }
/* ── HARDWARE ── */ .hw-card { background: var(–surface); border: 1px solid var(–border); border-radius: 10px; padding: 20px 24px; display: flex; align-items: center; gap: 20px; flex-wrap: wrap; } .hw-badge { background: linear-gradient(135deg, #1a1a30, #12122a); border: 1px solid rgba(62,207,207,0.25); border-radius: 8px; padding: 10px 18px; text-align: center; } .hw-badge .big { font-size: 22px; font-weight: 800; color: var(–accent2); } .hw-badge .small { font-size: 10px; font-family: ‘Space Mono’, monospace; color: var(–muted); letter-spacing: 0.1em; text-transform: uppercase; margin-top: 2px; } .hw-detail { font-size: 14px; color: #b0b0cc; line-height: 1.7; } .hw-detail strong { color: var(–text); }
/* ── LIMITATIONS ── */ .limit-list { list-style: none; display: grid; gap: 10px; } .limit-list li { background: var(–surface); border: 1px solid var(–border); border-left: 3px solid var(–accent3); border-radius: 8px; padding: 12px 18px; font-size: 14px; color: #c5c5df; line-height: 1.6; } .limit-list li::before { content: ’⚠ ’; color: var(–accent3); }
/* ── LICENSE BADGE ── */ .license-badge { display: inline-flex; align-items: center; gap: 8px; background: rgba(123,94,167,0.12); border: 1px solid rgba(123,94,167,0.35); border-radius: 6px; padding: 8px 16px; font-family: ‘Space Mono’, monospace; font-size: 12px; color: var(–accent); }
/* ── FOOTER ── */ .footer { margin-top: 64px; padding-top: 24px; border-top: 1px solid var(–border); display: flex; justify-content: space-between; align-items: center; flex-wrap: gap; font-family: ‘Space Mono’, monospace; font-size: 11px; color: var(–muted); gap: 12px; } .footer a { color: var(–accent2); text-decoration: none; } .footer a:hover { text-decoration: underline; }
@media (max-width: 600px) { .stat-row { grid-template-columns: 1fr 1fr; } .training-grid { grid-template-columns: 1fr 1fr; } } </style>
</head>
DistilBERT-Gemini3.2-Pro
Fast.Coder.NSFW-0.1B
<div class="tag-row">
<span class="tag blue">Text Generation</span>
<span class="tag blue">Causal LM</span>
<span class="tag purple">Reasoning</span>
<span class="tag purple">Coding</span>
<span class="tag purple">Creative Writing</span>
<span class="tag">Distillation</span>
<span class="tag">PyTorch</span>
<span class="tag">Safetensors</span>
<span class="tag">English</span>
<span class="tag hot">Not-For-All-Audiences</span>
</div>
<div class="stat-row">
<div class="stat-card">
<div class="stat-label">Parameters</div>
<div class="stat-value">81.9<span class="unit">M</span></div>
</div>
<div class="stat-card">
<div class="stat-label">Tensor Type</div>
<div class="stat-value" style="font-size:16px;">F32</div>
</div>
<div class="stat-card">
<div class="stat-label">Context Window</div>
<div class="stat-value">1024<span class="unit">tok</span></div>
</div>
<div class="stat-card">
<div class="stat-label">License</div>
<div class="stat-value" style="font-size:15px;">Apache 2.0</div>
</div>
<div class="stat-card">
<div class="stat-label">Base Model</div>
<div class="stat-value" style="font-size:14px;">DistilGPT2</div>
</div>
</div>
Model Description
DistilBERT-Gemini3.2-Pro.Fast.Coder.NSFW-0.1B is a fully fine-tuned version of DistilGPT2 — exposed to an aggressive curriculum of high-reasoning Gemini distillation traces, comprehensive coding datasets, creative writing frameworks, and mature internet discourse.
The model is designed to act as a highly responsive, analytical engine capable of deep structural reasoning and complex logic emulation. Despite its compact 81.9M parameter footprint, it targets multi-domain competence across code generation, reasoning chains, and open-ended text generation.
Trained natively at an accelerated maximum learning rate with a cosine decay schedule, the model synthesizes diverse programmatic and theoretical domains from a massive multi-repository corpus, processed at DistilGPT2's maximum context window of 1024 tokens.
Intended Use
Training Datasets
Fine-tuned on a synthesized Golden Mix drawn from eight multi-domain repositories:
Training Procedure
The model underwent full fine-tuning — no adapters or LoRA. All native DistilGPT2 parameters were globally updated. The training harness dynamically parsed heavily nested dataset repositories, enforcing a strict shape constraint to generate mathematically perfect 1024-token continuous sequences, maxing out the model's context window.
<!-- Hardware -->
<div class="hw-card">
<div class="hw-badge">
<div class="big">2×</div>
<div class="small">NVIDIA T4</div>
</div>
<div class="hw-detail">
<strong>Environment:</strong> Kaggle<br>
<strong>VRAM:</strong> 15 GB per GPU (30 GB total)<br>
<strong>Accelerator:</strong> Dual NVIDIA T4 GPUs
</div>
</div>
Limitations & Risks
- Small capacity: At ~82M parameters and a 1024-token context window, reasoning depth is fundamentally constrained relative to larger distillation targets.
- Single-epoch training: One epoch over the corpus limits generalization; the model may exhibit dataset memorization artifacts.
- NSFW content: Trained on adult Reddit data — outputs may be explicit or harmful without appropriate filtering in downstream applications.
- Unverified distillation data: Gemini traces from community datasets are not officially verified; quality and accuracy cannot be guaranteed.
- English-only: The model is not designed for multilingual use cases.
- Not production-ready: Intended as a research/experimental model; apply safety filtering before any user-facing deployment.
License & Attribution
Released under the Apache 2.0 license. Free to use, modify, and distribute with attribution.
Base model: distilbert/distilgpt2 by HuggingFace / DistilBERT team. Model card authored for: WithinUsAI.