FINAL-Bench / Darwin-4B-David

Darwin-4B-David — The First Second-Generation Darwin Model

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"

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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.

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Family Tree

Generation Comparison

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Parent Models

Model Diagnostic Scan (MDS)

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.

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Benchmarks

Key Results

GPQA Diamond Evaluation Details

GPQA Diamond (graduate-level scientific reasoning) was evaluated using generative (thinking mode) evaluation .

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
• 50 questions × 8 samples = 400 total generations, providing sufficient statistical confidence
• Full 198-question evaluation is planned

Note on lm-eval Loglikelihood Results

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.

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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.

Parent Comparison

Evolution Parameters

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Darwin V6 vs Conventional Merging

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Significance of Second-Generation Evolution

1. 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.

2. 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.

3. 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.

4. Multimodal preservation : Father's vision encoder ( 150M) and audio encoder ( 300M) are frozen during evolution, maintaining image/video/audio input capabilities.

5. Community synergy : Mother model creator DavidAU is an active contributor on HuggingFace. Darwin-4B-David symbolizes collaborative evolution within the open-source ecosystem.

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Model Specifications

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Usage

Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-4B-David", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "FINAL-Bench/Darwin-4B-David",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [{"role": "user", "content": "Prove that sqrt(2) is irrational."}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=True
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=4096, do_sample=False)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Disable Thinking Mode

text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
)

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VRAM Requirements

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Darwin Opus Family

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Roadmap

• Full 198-question GPQA Diamond evaluation (maj@8)
• MTI (Minimal Test-Time Intervention) serving — expected additional +9-11% reasoning accuracy
• GRPO + TinyLoRA reinforcement learning
• SSD self-distillation
• Cross-architecture breeding research (Transformer × Mamba FFN transplantation)

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References

• DARE-TIES: Yadav et al., 2023 ( https://arxiv.org/abs/2311.03099 ) — re-implemented, not library-dependent
• Darwin V6 Engine: https://huggingface.co/spaces/ginigen-ai/DARWIN-V5-BACKUP
• FINAL Bench: https://huggingface.co/spaces/FINAL-Bench/Leaderboard
• DavidAU DECKARD Series: https://huggingface.co/DavidAU
• MTI: Minimal Test-Time Intervention (arXiv:2510.13940)

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Built By

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Citation

@misc{vidraft_darwin_4b_david_2026,
  title        = {Darwin-4B-David: First Second-Generation Evolutionary Merge Model},
  subtitle     = {Recursive Evolution Achieves 85\% GPQA Diamond with 4.5B Parameters},
  author       = {VIDRAFT},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/FINAL-Bench/Darwin-4B-David}}
}