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john-broadway/Llama-3.2-3B-RYS-21-24-GGUF
Llama-3.2-3B-RYS-21-24-GGUF is a machine learning model from john-broadway. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as llama3.2.
Llama-3.2-3B-Instruct with layers 21-23 duplicated. A late-stack math circuit runs twice on every forward pass.
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From the Hugging Face model README
Llama-3.2-3B-Instruct with layers 21-23 duplicated. A late-stack math circuit runs twice on every forward pass.
28 base layers → 31 after duplication. No training, no merging, no weight changes.
Math 0.470 → 0.6132 (+14.32 — the biggest math lift in the v2 corpus). Reasoning 88.24% → 82.36% (−5.88). EQ 84.38 → 84.11 (−0.27).
| Metric | Baseline | RYS (21,24) | Delta |
|---|---|---|---|
| Math | 0.470 | 0.6132 | +14.32 |
| EQ | 84.38 | 84.11 | −0.27 |
| Reasoning | 88.24% | 82.36% | −5.88 |
The math amplifier. Llama-3.2-3B-Instruct has the second-highest baseline reasoning in the v2 corpus (88.24%) — near-ceiling, so RYS has little reasoning room to lift. But the same train-free intervention applied to the late-stack block (21,24) produces the biggest math lift anywhere in the corpus (+14.32 absolute, ~30% relative). Math and reasoning circuits sit at different depths in this model; the math one has headroom.
Pick this when math throughput matters and reasoning is already strong enough. The within-family contrast is the loudest in the corpus: sibling Llama-3.2-1B-RYS-10-13-GGUF lifts reasoning from 0% → 64.71% on the same block-duplication mechanism applied to a different depth.
llama-server -m Llama-3.2-3B-RYS-21-24-Q4_K_M.gguf -ngl 99
54 configurations tested. (21,24) block-3 is the best-combined pick (math-optimal). Full per-config sweep + cross-architecture analysis: v2 dataset.
Part of the RYS Sovereign Collection v2.
v1 — Qwen2.5 cross-scale + Qwen3-32B headline crossover. 5 model repos.
v2 — cross-architecture corpus. 21 model variants across 10 architecture families. Inverse correlation (r = −0.726): weak baselines lift more, in their weakest dimension. The Llama-3.2 family alone (1B + 3B) spans the entire baseline-vs-magnitude curve in the v2 corpus. 13 deployable RYS-applied weight repos covering every non-zero-lift variant.
Within-family sibling: john-broadway/Llama-3.2-1B-RYS-10-13-GGUF — the 0%→64.71% reasoning unlock at the weak-baseline end.
Credit
John Broadway, with collaboration from Claude (Opus 4.6 in April 2026 sweep generation and build pipeline; Opus 4.7 in May 2026 cross-architecture analysis and publication). Original RYS method by David Ng on Qwen2-72B; sweep + probe toolkit by alainnothere.