All benchmarks use generative evaluation via llama-server chat completions. Multiple-choice tasks (MMLU, ARC, HellaSwag) use letter extraction instead of loglikelihood scoring -- results are internally consistent for model comparison but absolute scores may differ from logprob-based evaluations by 5-15%.
Summary
Benchmark
Score
Metric
MMLU
78.56%
accuracy
ARC-Challenge
94.62%
accuracy
HellaSwag
74.49%
accuracy
HumanEval
94.51%
pass@1
GSM8K
94.77%
exact_match
MMLU Breakdown by Category
Category
Score
Correct / Total
Stem
89.83%
2,711 / 3,018
Humanities
77.45%
2,456 / 3,171
Social Sciences
84.45%
2,796 / 3,311
Other
67.55%
3,068 / 4,542
Speed Benchmarks
Measured with llama-bench. All layers GPU-offloaded (-ngl 99).
Prompt Processing (tokens/s)
Context Length
Speed
+/-sigma
128
7,172
73.0
512
16,646
117.8
2048
13,456
36.4
4096
11,684
28.1
8192
9,408
22.7
16384
6,669
4.6
Generation (tokens/s)
Metric
Speed
+/-sigma
tg128
367.4
0.9
Methodology
Evaluation Framework
Custom generative evaluators built for this rig. All benchmarks run through llama-server's /v1/chat/completions endpoint.
