The Hugging Face community continues to thrive with numerous powerful models. Based on the latest leaderboard, here are the top five machine learning models, showcasing their capabilities and performance metrics.
1. WizardCoder-15B-V1.0
Parameters 15.0 billion Win Rate 11.54% Average Score 142.09 Throughput 43.7 tokens/s Sequence Length 8192 Languages Supported 86 Performance on HumanEval-Python 50.53% Performance on HumanEval-Java 35.77% Performance on HumanEval-JavaScript 41.91% Performance on HumanEval-CPP 38.95% Performance on HumanEval-PHP 39.34% Performance on HumanEval-Julia 33.98% Performance on HumanEval-D 12.14% Performance on HumanEval-Lua 27.85% Performance on HumanEval-R 22.53% Performance on HumanEval-Racket 13.39% Performance on HumanEval-Rust 33.74% Performance on HumanEval-Swift 27.06% Throughput at batch size 50 1470.0 tokens/s Peak Memory Usage 32,414 MB Model Link WizardCoder-15B-V1.0
2. StarCoder-15B
Parameters 15.0 billion Win Rate 9.65% Average Score 135.6 Throughput 43.9 tokens/s Sequence Length 8192 Languages Supported 86 Performance on HumanEval-Python 33.57% Performance on HumanEval-Java 30.22% Performance on HumanEval-JavaScript 30.79% Performance on HumanEval-CPP 31.55% Performance on HumanEval-PHP 26.08% Performance on HumanEval-Julia 23.02% Performance on HumanEval-D 13.57% Performance on HumanEval-Lua 23.89% Performance on HumanEval-R 15.5% Performance on HumanEval-Racket 0.07% Performance on HumanEval-Rust 21.84% Performance on HumanEval-Swift 22.74% Throughput at batch size 50 1,490.0 tokens/s Peak Memory Usage 33,461 MB Model Link StarCoder-15B
3. StarCoderBase-15B
Parameters 15.0 billion Win Rate 9.54% Average Score 132.98 Throughput 43.8 tokens/s Sequence Length 8192 Languages Supported 86 Performance on HumanEval-Python 30.35% Performance on HumanEval-Java 28.53% Performance on HumanEval-JavaScript 31.7% Performance on HumanEval-CPP 30.56% Performance on HumanEval-PHP 26.75% Performance on HumanEval-Julia 21.09% Performance on HumanEval-D 10.01% Performance on HumanEval-Lua 26.61% Performance on HumanEval-R 10.18% Performance on HumanEval-Racket 11.77% Performance on HumanEval-Rust 24.46% Performance on HumanEval-Swift 16.74% Throughput at batch size 50 1,460.0 tokens/s Peak Memory Usage 32,366 MB Model Link StarCoderBase-15B
4. CodeGeex2-6B
Parameters 6.0 billion Win Rate 8.38% Average Score 104.29 Throughput 32.7 tokens/s Sequence Length 8192 Languages Supported 100 Performance on HumanEval-Python 34.54% Performance on HumanEval-Java 23.46% Performance on HumanEval-JavaScript 29.9% Performance on HumanEval-CPP 28.45% Performance on HumanEval-PHP 25.27% Performance on HumanEval-Julia 20.93% Performance on HumanEval-D 8.44% Performance on HumanEval-Lua 15.94% Performance on HumanEval-R 14.58% Performance on HumanEval-Racket 11.75% Performance on HumanEval-Rust 20.45% Performance on HumanEval-Swift 22.06% Throughput at batch size 50 1,100.0 tokens/s Peak Memory Usage 14,110 MB Model Link CodeGeex2-6B
5. StarCoderBase-7B
Parameters 7.0 billion Win Rate 8.15% Average Score 149.39 Throughput 46.9 tokens/s Sequence Length 8192 Languages Supported 86 Performance on HumanEval-Python 28.37% Performance on HumanEval-Java 24.44% Performance on HumanEval-JavaScript 27.35% Performance on HumanEval-CPP 23.3% Performance on HumanEval-PHP 22.12% Performance on HumanEval-Julia 21.77%
Conclusion
The landscape of machine learning is constantly evolving, and these models represent the pinnacle of current AI capabilities. From code generation to language processing, each model offers unique strengths that can serve various applications. Keep an eye on these models as they pave the way for future developments in AI.
