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| license: apache-2.0 |
| tags: |
| - convergentintel |
| - swarm-intelligence |
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| # S-AGI |
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| By Convergent Intelligence LLC: Research Division |
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| ## Convergent Intelligence Portfolio |
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| *Part of the [Standalone Models](https://huggingface.co/reaperdoesntknow) by [Convergent Intelligence LLC: Research Division](https://huggingface.co/reaperdoesntknow)* |
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| ### Related Models |
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| | Model | Downloads | Format | |
| |-------|-----------|--------| |
| | [SMOLM2Prover](https://huggingface.co/reaperdoesntknow/SMOLM2Prover) | 56 | HF | |
| | [SMOLM2Prover-GGUF](https://huggingface.co/reaperdoesntknow/SMOLM2Prover-GGUF) | 150 | GGUF | |
| | [DeepReasoning_1R](https://huggingface.co/reaperdoesntknow/DeepReasoning_1R) | 16 | HF | |
| | [SAGI](https://huggingface.co/reaperdoesntknow/SAGI) | 3 | HF | |
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| ### Top Models from Our Lab |
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| | Model | Downloads | |
| |-------|-----------| |
| | [Qwen3-1.7B-Thinking-Distil](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Thinking-Distil) | 501 | |
| | [LFM2.5-1.2B-Distilled-SFT](https://huggingface.co/reaperdoesntknow/LFM2.5-1.2B-Distilled-SFT) | 342 | |
| | [Qwen3-1.7B-Coder-Distilled-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) | 302 | |
| | [Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF) | 203 | |
| | [Qwen3-1.7B-Coder-Distilled-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT-GGUF) | 194 | |
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| **Total Portfolio: 41 models | 2,781 total downloads** |
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| *Last updated: 2026-03-28 12:58 UTC* |
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| ## Discrepancy Calculus Foundation |
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| This model is part of the [Convergent Intelligence LLC: Research Division](https://huggingface.co/reaperdoesntknow) portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model *should* produce and what it *actually* produces. |
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| DISC treats training singularities (loss plateaus, mode collapse, catastrophic forgetting) not as failures to be smoothed over, but as **structural signals** that reveal the geometry of the learning problem. Key concepts: |
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| - **Discrepancy Operator (D):** Measures the gap between expected and observed behavior at each training step |
| - **Jump Sets:** Boundaries where model behavior changes discontinuously — these are *features*, not bugs |
| - **Ghost Imprinting:** Teacher knowledge that transfers to student models through weight-space topology rather than explicit distillation signal |
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| For the full mathematical treatment, see [Discrepancy Calculus: Foundations and Core Theory](https://huggingface.co/reaperdoesntknow/Discrepancy_Calculus) (DOI: 10.57967/hf/8194). |
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| **Citation chain:** [Structure Over Scale](https://huggingface.co/reaperdoesntknow/Structure-Over-Scale) (DOI: 10.57967/hf/8165) → [Three Teachers to Dual Cognition](https://huggingface.co/reaperdoesntknow/DualMind_Methodolgy) (DOI: 10.57967/hf/8184) → [Discrepancy Calculus](https://huggingface.co/reaperdoesntknow/Discrepancy_Calculus) (DOI: 10.57967/hf/8194) |
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