Darwin-31B-Opus is a reasoning-enhanced model created by merging google/gemma-4-31B-it (Father) and TeichAI/gemma-4-31B-it-Claude-Opus-Distill (Mother) using the Darwin V6 engine.
Darwin V6 diagnoses both parent models at the tensor level before merging, assigning an independent optimal ratio to each of the 1,188 tensors. This is fundamentally different from conventional merging tools that apply a single uniform ratio across all tensors.
Claude 4.6 Opus high-effort reasoning distillation, code/science/analysis
Model Diagnostic Scan (MDS)
Father (gemma-4-31B-it) MDS Scan
Mother (Claude-Opus-Distill) MDS Scan
Left: Father (gemma-4-31B-it) — balanced generalist with low activation across most probes. Right: Mother (Claude-Opus-Distill) — strong REASONING concentration in L50-L60, CODE activation in late layers, KOREAN at start and end. The Mother shows significantly more specialized layer patterns from Claude Opus distillation.
Benchmarks
Benchmark
Darwin-31B-Opus
Father (gemma-4-31B-it)
Condition
ARC-Challenge
82.89%
-
loglikelihood, zero-shot, 200Q
GPQA Diamond
66.0%
60.0%
generative thinking mode, greedy, 50Q
GPQA Diamond was evaluated under identical conditions for both models: same 50 questions, same seed (i+42), same prompt template, greedy decoding (do_sample=False), max_new_tokens=2048, enable_thinking=True. Darwin-31B-Opus achieved a 10% relative improvement over the Father model.
Note: Gemma 4 architecture (Gemma4ForConditionalGeneration) has limited compatibility with lm-eval's loglikelihood method due to its multimodal wrapper structure. Only generative evaluation produces valid results for Gemma 4 based models. Full 198-question evaluation with Majority Voting is planned.
Darwin V6 vs Conventional Merging
Capability
mergekit (DARE-TIES)
Darwin V6
Implementation
Library call (mergekit CLI)
Direct PyTorch tensor operations, no external dependency
Ratio selection
Uniform ratio across all tensors
Per-tensor ratio from MDS diagnostic (1,188 independent ratios)
ratio < 0.15 → Father 100%, ratio > 0.85 → Mother 100% (zero interpolation noise)
Post-merge validation
Benchmark score only
Layer-by-layer Health Check: child vs both parents, interference and function loss detection
Search method
Manual tuning
CMA-ES evolution with adaptive 14-dimensional genome
Reproducibility
Config file
genome_hash seed guarantees identical output for identical genome
GPU efficiency
Single merge per run
Phase 1 proxy (200 steps, seconds) → Phase 2 real merge (top-k only evaluated)
How Darwin V6 Works
Darwin V6 does not use mergekit or any external merge library. It re-implements DARE-TIES (Yadav et al., 2023) directly via PyTorch tensor operations with per-tensor diagnostic ratios.
Before merging, Darwin performs a Model Diagnostic Scan (MDS) on both parents. For every tensor, it measures Shannon entropy (information density), standard deviation (activation spread), and L2 norm (energy). Additionally, 5 diagnostic probes (REASONING, CODE, MATH, KNOWLEDGE, LANGUAGE) are passed through the model, measuring cosine distance when each layer is skipped to determine functional importance.
The mri_trust parameter itself is optimized by the CMA-ES evolutionary algorithm, allowing the system to automatically determine the optimal balance between diagnostic prescription and evolutionary search for each model pair.
After merging, a Health Check compares the child model against both parents layer-by-layer, detecting interference (child importance >> parent max) or function loss (parent importance high but child dropped).
Key observations from the genome: ffn_ratio=0.93 indicates the FFN layers strongly favor the Mother (Claude Opus Distill), and block_5 (L50-L59)=0.86 shows the reasoning core layers also favor Mother. This aligns with the MDS heatmap pattern where Mother's reasoning capability concentrated in the final layers. Meanwhile, attn_ratio=0.32 preserves Father's attention structure, maintaining the original Gemma 4 multimodal and long-context capabilities.
