HumanEval+ ↗23.2%
Zephyr β-7B · HumanEval+ · Pass@1 · EvalPlus · official result data ↗
Reported model / MODEL EVIDENCE
Zephyr β-7B
4 benchmarks · 5 observations · 2 sources
Scores at a glance
4 benchmarks · large dot: Zephyr β-7B; small dots: closest models where reported
MBPP+ ↗34.7%
Zephyr β-7B · MBPP+ · Pass@1 · EvalPlus · official result data ↗
HumanEval ↗30%
Zephyr β-7B · HumanEval · Pass@1 · EvalPlus · official result data ↗
Zephyr-7B-β · MixEval & MixEval-Hard · accuracy · original comparison ↗
1 more reported result
69.1% · Zephyr-7B-β · MixEval & MixEval-Hard · accuracy · original comparison · MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures ↗
Closest models by score
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
gemma-7b-it →Google
3 of 4 shared benchmark comparisons
3 of 4 shared benchmark comparisons
3 of 4 shared benchmark comparisons
How similarity is measured
Mean absolute gap in percentile positions on shared benchmark settings; lower is closer. At least 60% overlap is required, with a minimum of three when available. Independent evaluations are used when there are at least three; otherwise published report comparisons are used. Repeated model observations within one setting are averaged before ranking. Writing-style traits and composite indices are excluded. This describes similarity, not an overall quality ranking.
Sources
Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.
- MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures — published result table ↗
All 8 authors
Jinjie Ni, Fuzhao Xue, Xiang Yue, Yuntian Deng, Mahir Shah, Kabir Jain, Graham Neubig, Yang You
- Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation — published result table ↗
Names used in source tables
Zephyr β-7B · Zephyr-7B-β