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reaperdoesntknow
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reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Part of the DistilQwen3 Series by Convergent Intelligence LLC: Research Division
This model is part of a distillation chain built on Discrepancy Calculus — a measure-theoretic framework where the teacher's output distribution is decomposed via the Mesh Fundamental Identity into smooth (AC), jump, and Cantor components. The discrepancy operator Df(x)=limε↓0ε1∫xx+ε quantifies local structural mismatch that standard KL divergence averages away.
Full theory: "On the Formal Analysis of Discrepancy Calculus" (Colca, 2026; Convergent Intelligence LLC: Research Division). Full methodology: Structure Over Scale (DOI: 10.57967/hf/8165).
| Model | Downloads | Format |
|---|---|---|
| DistilQwen3-1.7B-uncensored-GGUF | 122 | GGUF |
| Model | Downloads |
|---|---|
| Qwen3-1.7B-Thinking-Distil | 501 |
| LFM2.5-1.2B-Distilled-SFT | 342 |
| Qwen3-1.7B-Coder-Distilled-SFT | 302 |
| Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF | 203 |
| Qwen3-1.7B-Coder-Distilled-SFT-GGUF |
Total Portfolio: 41 models | 2,781 total downloads
Last updated: 2026-03-28 12:55 UTC
This model is part of the DistilQwen proof-weighted distillation series. Collection: 9 models | 2,788 downloads
| Teacher | Student Size | Strength | Models |
|---|---|---|---|
| Qwen3-30B-A3B (Instruct) | 1.7B | Instruction following, structured output, legal reasoning | 3 (833 DL) |
| Qwen3-30B-A3B (Thinking) | 0.6B | Extended deliberation, higher-entropy distributions, proof derivation | 3 (779 DL) |
| Qwen3-30B-A3B (Coder) | 1.7B | Structured decomposition, STEM derivation, logical inference | 2 (825 DL) |
The only BF16 collection in the portfolio. While the broader Convergent Intelligence catalog (43 models, 12,000+ downloads) was trained on CPU at FP32 for $24 total compute, the DistilQwen series was trained on H100 at BF16 with a 30B-parameter teacher. Same methodology, premium hardware. This is what happens when you give the pipeline real compute.
All models use proof-weighted knowledge distillation: 55% cross-entropy with decaying proof weights (2.5× → 1.5×), 45% KL divergence at T=2.0. The proof weight amplifies loss on reasoning-critical tokens, forcing the student to allocate capacity to structural understanding rather than surface-level pattern matching.
Full methodology: Structure Over Scale (DOI: 10.57967/hf/8165)
Part of the reaperdoesntknow research portfolio — 49 models, 22,598 total downloads | Last refreshed: 2026-03-30 12:05 UTC
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