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CodeGemma: Open Code Models Based on Gemma

About

This paper introduces CodeGemma, a collection of specialized open code models built on top of Gemma, capable of a variety of code and natural language generation tasks. We release three model variants. CodeGemma 7B pretrained (PT) and instruction-tuned (IT) variants have remarkably resilient natural language understanding, excel in mathematical reasoning, and match code capabilities of other open models. CodeGemma 2B is a state-of-the-art code completion model designed for fast code infilling and open-ended generation in latency-sensitive settings.

CodeGemma Team: Heri Zhao, Jeffrey Hui, Joshua Howland, Nam Nguyen, Siqi Zuo, Andrea Hu, Christopher A. Choquette-Choo, Jingyue Shen, Joe Kelley, Kshitij Bansal, Luke Vilnis, Mateo Wirth, Paul Michel, Peter Choy, Pratik Joshi, Ravin Kumar, Sarmad Hashmi, Shubham Agrawal, Zhitao Gong, Jane Fine, Tris Warkentin, Ale Jakse Hartman, Bin Ni, Kathy Korevec, Kelly Schaefer, Scott Huffman• 2024

Related benchmarks

TaskDatasetResultRank
Code GenerationHumanEval
Pass@154.88
1036
Code GenerationHumanEval+
Pass@141.46
383
Code GenerationMBPP+
Pass@154.76
216
Code GenerationMBPP
Pass@1 Accuracy53.2
59
Code GenerationLiveCodeBench
Pass@18.12
51
Code CompletionAPC Hard Completion 1.0 (test)
HCR100
33
Code CompletionAPC Placeholder Completion 1.0 (test)
PCR0.00e+0
33
Code GenerationDS-1000 1.0 (test)
Matplotlib54.7
19
Code GenerationBigCodeBench
pass@125.44
18
Assembly Code Reverse EngineeringREx86 (test)
CE5.4166
16
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