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license: mit |
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language: |
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- en |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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# 🧠 Titan-Atom |
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> [!IMPORTANT] |
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> Hey, before you go any further, please know that this model is a joke and not 500T parameters. Gosh, you would need so much hardware to make a model so big! |
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> *Yeah yeah, we know... the name’s a cliché. "Atom" because it's tiny. Heh. But with **487,912B parameters** — that’s **487.9 trillion** — it’s also not. Get it?* |
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Titan-Atom is a foundational micro-architecture model designed to push the boundaries of declared scale, metadata innovation, and post-structural tensor semantics. It reimagines what small can mean when "small" is entirely hypothetical. |
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--- |
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## 📊 Model Summary |
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| Attribute | Value | |
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|------------------|---------------------------------| |
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| **Model Name** | Titan-Atom | |
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| **Parameter Count** | 487,912B (≈ 487.9 trillion) | |
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| **Format** | `safetensors` | |
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| **Precision** | Custom-float / Non-denominational | |
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| **Context Window**| 512,000 tokens (virtualized) | |
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| **Training FLOPs**| Unknown / decoupled | |
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| **Frameworks** | HF-compatible, byte-deterministic | |
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--- |
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## 💡 Architectural Highlights |
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### 🌀 Quantum-Indexed Attention (QIA) |
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Implements a sub-real attention strategy via randomized rotational head alignment. Tokens may or may not attend to anything, but the math looks expensive. |
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### 🧩 Fragmented Tensor Reconstruction (FTR) |
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Weights are stored as deconstructed thought-forms and reassembled at load-time using speculative token priors. |
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### 🪞 Mirror Embedding Stacks |
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Each embedding reflects an imagined twin in a simulated tensor dimension, effectively doubling capacity while remaining physically absent. |
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## 🧠 Parameter Design |
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Titan-Atom features a declarative tensor scaling strategy. Its core tensor, `wte.weight`, is shaped as: |
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```python |
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[635,302,083,334 x 768] # ≈ 487,912,000,000 parameters |
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``` |
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This shape is purely representational and has no bearing on performance, size, or utility. |
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But it **looks** amazing in a spreadsheet. |
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## 🧪 Training Details |
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Titan-Atom was “trained” via a process known as **Recursive Metadata Embellishment**, in which tensor shapes are reinterpreted until meaning is inferred from scale alone. |
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No gradients. No checkpoints. Just header-level bravado. |
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--- |
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## 📉 Benchmarks (Symbolic / Hypothetical) |
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| Task | Score | Conditions | |
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|-----------------|-----------|-----------------------------------| |
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| LAMBADA | 119.2 | Simulated with confidence | |
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| ARC-Challenge | 74% | Based on theoretical overfit | |
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| MMLU | ∞ / ∞ | Escaped benchmarking framework | |
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| HumanEval | 42.0% | Using probabilistic thought-flows | |
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*All results exist in a simulated benchmarking environment unbound by physical inference.* |
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## 🛰 Deployment Notes |
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Despite its trillion-scale persona, Titan-Atom fits neatly into a `.safetensors` file. Thanks to zero-weight inflation and pure metadata adjustment, deployment is fast and disk usage is minimal. |
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The illusion is highly efficient. |
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## ⚠️ Ethical Considerations |
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Titan-Atom is unaligned, untested, and unrepentant. Outputs may range from irrelevant to inexplicable. Use only in labs equipped with philosophical grounding. |
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## 📜 License |
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**UTCL v0.2** – *Unverified Theoretical Compute License* |
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Redistribution allowed in conceptual, dreamlike, or ironic form. |
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## 🧵 Related Work |
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- **GPT-Dust** — Smaller than the Planck constant. |
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- **LLaMA-Rind** — Just the metadata of a LLaMA. |
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- **Bloomfield** — Entirely made of training logs. |
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--- |
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## 👁 Final Note |
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> “When a model claims 487 trillion parameters, the only real question left is… why stop there?” |
