ideogram-ai/ideogram-4-fp8
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š§¬ Introducing Darwin-9B-NEG ā the first model with Native Entropy Gating (NEG)
š Try it now: FINAL-Bench/Darwin-9B-NEG
š Q4 bit : FINAL-Bench/Darwin-9B-MFP4
We're thrilled to release Darwin-9B-NEG, a 9B-parameter reasoning model
that embeds an architecturally-internalised sense of self-confidence directly
into the transformer ā our proprietary Native Entropy Gating (NEG) technology.
š GPQA Diamond (198 PhD-level questions):
āø Baseline Darwin-9B (no NEG) ā 51.01 %
āø Pure NEG (greedy Ā· 1Ć cost) ā 63.64 % š„ +12.63 %p
āø + Permutation (4Ć cost) ā 76.26 %
āø + Ensemble Refinement (~20Ć) ā 84.34 % š
With only 9 billion parameters and 1Ć inference cost, Pure NEG jumps
+12.63 %p over the same model without NEG. Going all-in with ensemble
refinement pushes it to 84.34 % ā surpassing the published Qwen3.5-9B
leaderboard score (81.7 %) by +2.64 %p.
š¬ What makes NEG different from Multi-Turn Iteration (MTI)?
Classical MTI needs 3-8Ć extra inference passes. NEG instead lives
INSIDE the single decoding loop. Two tiny modules ride with the
transformer: NEG-Head predicts per-token entropy from the last hidden
state, and NEG-Gate conditionally restricts the top-k choice when
confidence is low. The gate activates in only 4.36 % of tokens ā
essentially free at inference time.
āØ Key differentiators
ā¢ Architecturally internalised ā model file *is* the feature
ā¢ 1Ć inference cost (vs. 3-8Ć for MTI)
ā¢ Drop-in with vLLM / SGLang / TGI / transformers ā no extra engine
ā¢ +12.63 %p reasoning at zero latency overhead
ā¢ Single-file deployment, Apache 2.0 licensed
š§¬ Lineage
Qwen/Qwen3.5-9B ā Darwin-9B-Opus (V7 evolutionary merge) ā Darwin-9B-NEG (V8 + NEG training)
#Darwin #NEG #NativeEntropyGating #GPQA #Reasoning #LLM #OpenSource #Apache2
š Try it now: FINAL-Bench/Darwin-9B-NEG
š Q4 bit : FINAL-Bench/Darwin-9B-MFP4
We're thrilled to release Darwin-9B-NEG, a 9B-parameter reasoning model
that embeds an architecturally-internalised sense of self-confidence directly
into the transformer ā our proprietary Native Entropy Gating (NEG) technology.
š GPQA Diamond (198 PhD-level questions):
āø Baseline Darwin-9B (no NEG) ā 51.01 %
āø Pure NEG (greedy Ā· 1Ć cost) ā 63.64 % š„ +12.63 %p
āø + Permutation (4Ć cost) ā 76.26 %
āø + Ensemble Refinement (~20Ć) ā 84.34 % š
With only 9 billion parameters and 1Ć inference cost, Pure NEG jumps
+12.63 %p over the same model without NEG. Going all-in with ensemble
refinement pushes it to 84.34 % ā surpassing the published Qwen3.5-9B
leaderboard score (81.7 %) by +2.64 %p.
š¬ What makes NEG different from Multi-Turn Iteration (MTI)?
Classical MTI needs 3-8Ć extra inference passes. NEG instead lives
INSIDE the single decoding loop. Two tiny modules ride with the
transformer: NEG-Head predicts per-token entropy from the last hidden
state, and NEG-Gate conditionally restricts the top-k choice when
confidence is low. The gate activates in only 4.36 % of tokens ā
essentially free at inference time.
āØ Key differentiators
ā¢ Architecturally internalised ā model file *is* the feature
ā¢ 1Ć inference cost (vs. 3-8Ć for MTI)
ā¢ Drop-in with vLLM / SGLang / TGI / transformers ā no extra engine
ā¢ +12.63 %p reasoning at zero latency overhead
ā¢ Single-file deployment, Apache 2.0 licensed
š§¬ Lineage
Qwen/Qwen3.5-9B ā Darwin-9B-Opus (V7 evolutionary merge) ā Darwin-9B-NEG (V8 + NEG training)
#Darwin #NEG #NativeEntropyGating #GPQA #Reasoning #LLM #OpenSource #Apache2
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