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FINAL-Bench
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Model Details
Model Provider
FINAL-Bench
Model Tree
Base
Qwen/Qwen3-4B
Fine-tuned
this model
Input Modalities
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Output Modalities
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Supported Functionality
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Container
| Gen | Model | Composition |
|---|---|---|
| Gen 1 | Darwin-Chimera-4B-Gen1 | Qwen3-4B attention-healing adapter (FFN = Qwen3-4B, frozen) |
| Gen 2 (this) | Darwin-V9-Chimera-4B | Gen1 adapter + Gemma4-E4B FFN crossbreed → re-healing |
The Gen-1 adapter's FFN is reconstructed by cross-breeding Qwen3-4B FFN with Gemma4-E4B FFN (ratio 0.15), then the attention is re-healed (VIDRAFT) to adapt to the fused FFN. This carries the Gen-1 attention forward while blending a second model's knowledge — so the result is not reducible to any single parent.
| model | KMMLU | stage |
|---|---|---|
| Qwen3-4B (original) | 13.3% | base |
| Gemma4-E4B (base) | 26.7% | base |
| Darwin-Chimera Gen1 | 27.1%* | base |
| fused raw (pre re-heal) | 22.2% | intermediate |
| Darwin-V9 (this) | 27.8% | base |
* Gen1 measured on 6 subjects. All numbers are base zero-shot — instruction-following quality is expected from a later SFT stage (cf. Gemma4-E4B base 26.7% → it 69.4%).
→ After blending 15% Gemma4 FFN, performance is maintained / slightly above the Gen-1 baseline and Gemma4-E4B base. Gemma knowledge is visibly incorporated (multilingual facts, "Germany is Berlin / Italy is ..."), and the intermediate English degradation is recovered by re-healing.
Gemma Terms of Use (Gemma4-E4B weights are blended in) + Apache 2.0 (Qwen3-4B). Built on Qwen/Qwen3-4B and google/gemma-4-E4B.