Juno-N-Coder-25B is a coding-specialised derivative of
NVIDIA Nemotron 3.5 Lightning 30B, produced by
structured expert pruning with our SPACE algorithm (Specialisation Pruning Algorithm for
Compression of Experts). This is the first model in the Juno series developed to run on-prem on the Locai One.
Locai Labs was one of NVIDIA's early-access partners for Nemotron 3.5 Lightning, and we want to thank the NVIDIA team for giving us early access to the model and supporting our work.
The goal with Juno-N-Coder was to create a coding-specialised version of Nemotron 3.5 Lightning purely through pruning and without any recovery fine-tuning. Using SPACE, we evaluated each expert's contribution to the target capability, in this case software development, removing the bottom 25% and renormalising the router so that the gate distribution remains correctly scaled.
The results are summarised in the model's performance profile below. Juno-N retains performance across four software engineering benchmarks, while degrading in general knowledge and factual recall as measured by MMLU Redux and SimpleQA.
Spider diagram
Benchmark
Juno-N-Coder-25B
Nemotron 3.5 Lightning 30B
LiveCodeBench v6 (test_v6_2408_2505)
77.09
77.97
HumanEval+
90.55
89.33
MBPP+
80.29
81.49
SciCode (subtask)
36.98
36.09
MMLU Redux
82.18
90.00
SimpleQA (rubric)
37.88
47.95
MMLU Redux subject group
Juno-N-Coder-25B
Base
Δ
Maths & CS
94.6
94.9
-0.3
Physical sciences
88.3
94.4
-6.1
Economics
78.7
87.4
-8.7
Medicine
72.7
84.4
-11.7
World facts
71.7
84.0
-12.3
Humanities
75.9
88.5
-12.6
All models were evaluated using NVIDIA NeMo Evaluator at identical settings for both models:
temperature 1.0, top_p 0.95, a 65,536-token generation limit, BF16 weights, and
reasoning traces separated from the response before scoring.