Two modified versions of Qwen3.5-2B-Instruct produced by
RYS layer duplication — no training, no weight changes, just routing hidden states through a specific circuit twice.
Scores from an internal sweep benchmark run during circuit search. Sample sizes are small — treat these as directional indicators, not definitive benchmarks.
Model
Math
EQ
Reasoning
Base (24 layers)
0.188
0.0
0.118
rys_8-11 (28 layers)
0.125
11.3
0.176
rys_7-10_reasoning (28 layers)
0.062
0.0
0.294
Math: Ng's partial-credit scoring on a small GSM8K sample
Reasoning: fraction correct across causal, date, logic, navigation, and GSM8K probes
rys_8-11 shows the best combined improvement: EQ rises from 0 to 11.3 and reasoning improves. rys_7-10_reasoning achieves the highest reasoning score (0.294 vs 0.118 baseline) but at the cost of math and EQ.
What is RYS?
Transformers self-organise during training into functional circuits — contiguous blocks of layers that act together. The RYS technique duplicates a specific block in the forward pass using the same weights, with no extra copies on disk beyond the GGUF file overhead:
The model processes the circuit twice, without any weight changes or fine-tuning.
Hybrid Mamba/attention architecture constraint
Qwen3.5-2B is a hybrid SSM/attention model (full_attention_interval = 4): full attention every 4th layer, Gated DeltaNet SSM everywhere else. The architecture repeats 6 times:
3 × (DeltaNet → FFN) → 1 × (Attention → FFN)
This creates a hard constraint on layer surgery: the total layer count must remain divisible by 4.
rys_8-11 duplicates layers 8–11, one complete DeltaNet+Attention unit. rys_7-10_reasoning duplicates layers 7–10, spanning the boundary between two attention intervals.
How the circuit was found
A two-pass sweep over the 24-layer model:
Pass 1 — 8-layer blocks, stride 4, layers 0–16:
(4, 12) identified as the hot zone: EQ=11.76, reasoning=0.176
Pass 2 — 4-layer blocks, stride 1, layers 4–16:
(8, 12) best combined: EQ=11.33, reasoning=0.176
(7, 11) best reasoning: reasoning=0.294
Each configuration was tested by patching the GGUF layer path, loading with llama-server, and scoring with the probe suite.
Usage
llama.cpp / llama-server
bash
1# Best combined (EQ + reasoning)2llama-server -m Qwen3.5-2B-rys_8-11-UD-Q4_K_XL.gguf -ngl 99 --port 808034# Best reasoning5llama-server -m Qwen3.5-2B-rys_7-10_reasoning-UD-Q4_K_XL.gguf -ngl 99 --port 8080
Thinking mode
Qwen3.5 defaults to thinking mode (<think>…</think>). Add /no_think to the system prompt for fast, direct answers:
