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Model page: CodeGemma
Resources and technical documentation:
Authors: Google
CodeGemma is a family of lightweight open code models built on top of Gemma. CodeGemma models are text-to-text and text-to-code decoder-only models and are available as a 7 billion pre-trained variant that specializes in code completion and code generation tasks, a 7 billion parameter instruction-tuned variant for code chat and instruction following and a 2 billion parameter pre-trained variant for fast code completion.
CodeGemma models are optimized with NeMo Framework.
NVIDIA releases CodeGemma checkpoints converted to the *.nemo
format to allow users to quickly customize them using techniques offered by NeMo Framework.
Code Gemma customization examples are available at GenerativeAIExamples.
Input: For pre-trained model variants: code prefix and optionally suffix for code completion and generation scenarios or natural language text/prompt. For instruction tuned model variant: natural language text or prompt.
Output: For pre-trained model variants: fill-in-the-middle code completion, code and natural language. For instruction tuned model variant: code and natural language.
@article{codegemma_2024,
title={CodeGemma: Open Code Models Based on Gemma},
url={https://www.kaggle.com/m/3301},
author={CodeGemma Team and Hartman, Ale Jakse and Hu, Andrea and Choquette-Choo, Christopher A. and Zhao, Heri and Fine, Jane and Hui,
Jeffrey and Shen, Jingyue and Kelley, Joe and Howland, Joshua and Bansal, Kshitij and Vilnis, Luke and Wirth, Mateo and Nguyen, Nam, and Michel, Paul and Choy, Peter and Joshi, Pratik and Kumar, Ravin and Hashmi, Sarmad and Agrawal, Shubham and Zuo, Siqi and Warkentin, Tris and Gong, Zhitao et al.},
year={2024}
}
Using Gemma as the base model, CodeGemma 2B and 7B pre-trained variants are further trained on an additional 500 billion tokens of primarily English language data from open source mathematics datasets and synthetically generated code.
The following data pre-processing techniques were applied to train CodeGemma:
Benchmark | 2B | 7B | 7B-IT |
---|---|---|---|
HumanEval | 31.1 | 44.5 | 56.1 |
MBPP | 43.6 | 56.2 | 54.2 |
HumanEval Single Line | 78.41 | 76.09 | 68.25 |
HumanEval Multi Line | 51.44 | 58.44 | 20.05 |
BC HE C++ | 24.2 | 32.9 | 42.2 |
BC HE C# | 10.6 | 22.4 | 26.7 |
BC HE Go | 20.5 | 21.7 | 28.6 |
BC HE Java | 29.2 | 41.0 | 48.4 |
BC HE JavaScript | 21.7 | 39.8 | 46.0 |
BC HE Kotlin | 28.0 | 39.8 | 51.6 |
BC HE Python | 21.7 | 42.2 | 48.4 |
BC HE Rust | 26.7 | 34.1 | 36.0 |
BC MBPP C++ | 47.1 | 53.8 | 56.7 |
BC MBPP C# | 28.7 | 32.5 | 41.2 |
BC MBPP Go | 45.6 | 43.3 | 46.2 |
BC MBPP Java | 41.8 | 50.3 | 57.3 |
BC MBPP JavaScript | 45.3 | 58.2 | 61.4 |
BC MBPP Kotlin | 46.8 | 54.7 | 59.9 |
BC MBPP Python | 38.6 | 59.1 | 62.0 |
BC MBPP Rust | 45.3 | 52.9 | 53.5 |
Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including:
Human evaluation on prompts covering content safety and representational harms. See the Gemma model card for more details on evaluation approach.
Specific testing of cyber-offence capabilities, focusing on testing autonomous hacking capabilities and ensuring potential harms are limited.
The results of ethics and safety evaluations are within acceptable thresholds for meeting internal policies for categories such as child safety, content safety, representational harms, memorization, large-scale harms. See the Gemma model card for more details.
Large Language Models (LLMs) have limitations based on their training data and the inherent limitations of the technology. See the Gemma model card for more details on the limitations of LLMs.
The development of large language models (LLMs) raises several ethical concerns. We have carefully considered multiple aspects in the development of these models.
Please refer to the same discussion in the Gemma model card for model details.
Code Gemma models have a wide range of applications, which vary between IT and PT models. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development.
At the time of release, this family of models provides high-performance open code-focused large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models.
Using the coding benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives.
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