20 KiB
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</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.