Darwin-4B-David

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

python
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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

python
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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

bibtex
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@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}}
}

This model is introduced in Darwin Family.