Gemma 4 E4B Dense | 4.5B Params | Thinking Mode | 128K Context | 140+ Languages | BF16 | Apache 2.0 The first-ever second-generation Darwin model — "Evolution of Evolution"
Overview
Darwin-4B-David is the first second-generation (Generation 2) model in Darwin history — a model evolved from an already-evolved model.
The first-generation Darwin-4B-Opus (Father) was evolved from the original gemma-4-E4B-it using the Darwin V6 engine. Darwin-4B-David was born by crossbreeding this first-generation evolved model with DavidAU's DECKARD-Expresso-Universe (Mother). This is the first realization of Darwin's core concept: "Merge = Evolve" applied recursively.
The name "David" pays tribute to the Mother model's creator DavidAU, while evoking the biblical David who defeated Goliath — symbolizing how a 4.5B small model challenges models many times its size.
Left: Father (Darwin-4B-Opus) — REASONING concentration in later layers (dist 0.4), MATH activation throughout. Already optimized through Gen-1 evolution. Right: Mother (DECKARD-Expresso-Universe) — Strong KOREAN hotspot (dist 1.5), signature of Unsloth deep tuning. Remaining regions show uniform distribution.
Benchmarks
Key Results
Benchmark
gemma-4-E4B-it (Original)
Darwin-4B-David (Gen-2)
Improvement
Conditions
GPQA Diamond
58.6%
85.0%
+26.4%p
Generative, maj@8, 50Q sampling
ARC-Challenge
64.93%
64.93%
±0
25-shot, chat template, BF16, loglikelihood
KMMLU
48.47%
48.46%
±0
5-shot, 225Q, loglikelihood
GPQA Diamond Evaluation Details
GPQA Diamond (graduate-level scientific reasoning) was evaluated using generative (thinking mode) evaluation.
Setting
Value
Dataset
Idavidrein/gpqa, gpqa_diamond split
Questions
50 (sampled from 198 total)
Evaluation method
maj@8 (8 independent generations per question, majority vote determines final answer)
Prompt format
Epoch AI standard (ANSWER: LETTER)
Thinking mode
Enabled (chat_template, enable_thinking)
max_new_tokens
4,096
temperature
1.0
top_p / top_k
0.95 / 64
Precision
BF16
Choice shuffling
Fixed seed per question (MD5 hash)
Why maj@8:
Single-sample (greedy/pass@1) is vulnerable to stochastic variation with do_sample
8 independent generations with majority voting reflects the model's stable reasoning capability
maj@k is standard practice in frontier model benchmarks (AIME, MATH, etc.)
Note on 50-question sampling:
GPQA Diamond contains 198 questions total; 50 questions represent 25.3% of the full set
ARC-Challenge and KMMLU show identical scores to the original model. This is characteristic of DARE-TIES merging: the loglikelihood method compares token probabilities across answer choices and does not capture differences in generation quality, reasoning chains, or creativity. The evolution effect is clearly visible in generative evaluation (GPQA Diamond), where the difference emerges during step-by-step thinking mode reasoning.
MRI-Guided Evolution Recipe
Darwin V6's Model MRI scanned weight divergence across all 42 layers and automatically assigned independent weight ratios to each layer.
Layer Range
Weight
Strategy
Layer 0-3
0.81
Absorb Mother's embedding-adjacent layers
Layer 15-16
0.91
Maximum Mother creativity/character layer reinforcement
Layer 22-25
0.95
Maximum absorption of Mother's KOREAN hotspot
Layer 26-27
0.40
Father priority preservation zone
Layer 30-40
0.48
Father REASONING/MATH preservation
Layer 40-42
0.62
Output layer balance
Parent Comparison
Father vs Mother layer-wise importance comparison
Evolution Parameters
Setting
Value
Merge method
DARE-TIES (direct PyTorch, no mergekit dependency)
Density
0.800 ~ 0.850
Normalization
normalize: true
Evolution method
Darwin mergekit (MRI-guided)
Population size
20
Phase 1 (proxy search)
200 steps
Phase 2 (real merge)
10 steps, top 5 elite
Fitness function
kmmlu_lite (Korean knowledge)
Best fitness
0.8412 (84.12%)
Total time
45.3 minutes (H100 ×1)
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 (independent ratios per tensor)
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 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)
Significance of Second-Generation Evolution
Proof of "Evolution of Evolution": The first systematic case of recursive evolution (2+ generations) in the open-source model merging community. Darwin V6 + MRI automates the entire process.
85% GPQA Diamond at 4.5B parameters: +26.4%p over the original 58.6%. This surpasses the 31B-class gemma-4-31B (84.3%) with only 4.5B parameters — an exceptional result in parameter efficiency.
Apache 2.0 + Edge deployment: Preserves the Gemma 4 E4B architecture, enabling deployment on Jetson Orin NX 16GB and consumer GPUs with no commercial restrictions.
Multimodal preservation: Father's vision encoder (~150M) and audio encoder (~300M) are frozen during evolution, maintaining image/video/audio input capabilities.
Community synergy: Mother model creator DavidAU is an active contributor on HuggingFace. Darwin-4B-David symbolizes collaborative evolution within the open-source ecosystem.
