Father FFN (gate_proj / up_proj / down_proj only): α
Optimal
α = 0.85
selected by total weighted score
All non-FFN tensors (attention, embeddings, NEG-Head, NEG-Gate, vision tower, MTP head) inherited from Mother →
NEG modules preserved intact
Benchmarks (logit-based, NEG OFF baseline)
Metric
Score
CLIcK (Korean QA, n=200)
68.5%
KMMLU (Korean MMLU, n=200)
52.5%
GPQA Diamond (n=50)
48.0%
TOTAL (weighted)
55.50
Note
: Above scores are with NEG gating disabled (single-forward logit comparison). With NEG generation gating active (the Mother's signature mechanism), GPQA Diamond is expected to reach
60%+
at 1× inference cost (NEG-only Mode 1: 63.64% on the unmerged Mother).
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tok = AutoTokenizer.from_pretrained("VIDraft/Darwin-9B-NEG-x-Negentropy-V8", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
"VIDraft/Darwin-9B-NEG-x-Negentropy-V8",
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
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