Darwin-V9-Chimera-4B-SFT (Generation 2 · instruction-tuned)
VIDRAFT attention + Qwen3-4B / Gemma4-E4B FFN crossbreed + SFT. A Qwen3-4B × Gemma4-E4B hybrid — NOT from-scratch. Private research checkpoint.
Lineage
| Gen | Model | Composition |
|---|---|---|
| Gen1 | Darwin-Chimera-4B-Gen1 | Qwen3-4B attention-healing adapter |
| Gen2 (healed) | Darwin-V9-Chimera-4B | + Gemma4-E4B FFN crossbreed → re-healing |
| Gen2 SFT (this) | Darwin-V9-Chimera-4B-SFT | + B4-Hybrid SFT (think toggle + ko/en CoT) |
SFT
- data: B4_merged_hybrid, 17,202 ex (think/no_think toggle, Korean/English CoT)
- method: ChatML + prompt masking (assistant-only loss) + full fine-tune, lr 2e-5, 3 epochs
Evaluation (KMMLU, ChatML)
| measure | score |
|---|---|
| max_new=8 (short answer) | 25.6% (measurement artifact) |
| max_new=768 (think) | 52.2% (Math 70%, CS 67%) |
→ With sufficient thinking budget, 52.2% — ~2× the healed checkpoint (27.8%). Instruction following is complete (think/no_think toggle, markdown, code). The short-answer score is an artifact: this model reasons before answering.
Weight fingerprint (honest)
- vs Qwen3-4B: FFN 16% rel-change, attention 3%, embed 0.78%. FFN cosine 0.998 → Qwen-dominant.
- vs Gemma4-E4B FFN: cosine 0.09 (15% blend, weak at weight level; Gemma knowledge partially visible in generation).
- At the weight level this is clearly a Qwen3-4B derivative. We make no from-scratch / independence claim.
NEG (Native Entropy Gating)
- A NEG-Head was trained (entropy-prediction pearson 0.92) and
neg_modules.safetensorsis included. - However, NEG showed no effect on KMMLU (threshold untuned / mid-difficulty). Potential effect on harder reasoning (GPQA) is unverified.
License
Gemma Terms of Use (Gemma4-E4B weights blended in) + Apache 2.0 (Qwen3-4B). Built on Qwen/Qwen3-4B and google/gemma-4-E4B.