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CodeGemma

Model Page : CodeGemma

Resources and Technical Documentation : Technical Report : Responsible Generative AI Toolkit

Terms of Use : Terms

Authors : Google

In llama.cpp, and other related tools such as Ollama and LM Studio, please make sure that you have these flags set correctly, especially repeat-penalty. Georgi Gerganov (llama.cpp's author) shared his experience in https://huggingface.co/google/gemma-7b-it/discussions/38#65d7b14adb51f7c160769fa1.

Description

CodeGemma is a collection 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 pretrained 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 pretrained variant for fast code completion.

codegemma-2b codegemma-7b codegemma-7b-it
Code Completion
Generation from natural language
Chat
Instruction Following

For detailed model card, refer to https://huggingface.co/google/codegemma-1.1-7b-it.

Sample Usage

$ cat non_prime
/// Write a rust function to identify non-prime numbers.
///
/// Examples:
/// >>> is_not_prime(2)
/// False
/// >>> is_not_prime(10)
/// True
pub fn is_not_prime(n: i32) -> bool {
$ main -m codegemma-1.1-7b-it.gguf --temp 0 --top-k 0 -f non_prime --log-disable --repeat-penalty 1.0
 /// Write a rust function to identify non-prime numbers.
///
/// Examples:
/// >>> is_not_prime(2)
/// False
/// >>> is_not_prime(10)
/// True
pub fn is_not_prime(n: i32) -> bool {
    if n <= 1 {
        return true;
    }
    for i in 2..=(n as f64).sqrt() as i32 {
        if n % i == 0 {
            return true;
        }
    }
    false
}

Coding Benchmarks

Benchmark 2B 2B (1.1) 7B 7B-IT 7B-IT (1.1)
HumanEval 31.1 37.8 44.5 56.1 60.4
MBPP 43.6 49.2 56.2 54.2 55.6
HumanEval Single Line 78.4 79.3 76.1 68.3 77.4
HumanEval Multi Line 51.4 51.0 58.4 20.1 23.7
BC HE C++ 24.2 19.9 32.9 42.2 46.6
BC HE C# 10.6 26.1 22.4 26.7 54.7
BC HE Go 20.5 18.0 21.7 28.6 34.2
BC HE Java 29.2 29.8 41.0 48.4 50.3
BC HE JavaScript 21.7 28.0 39.8 46.0 48.4
BC HE Kotlin 28.0 32.3 39.8 51.6 47.8
BC HE Python 21.7 36.6 42.2 48.4 54.0
BC HE Rust 26.7 24.2 34.1 36.0 37.3
BC MBPP C++ 47.1 38.9 53.8 56.7 63.5
BC MBPP C# 28.7 45.3 32.5 41.2 62.0
BC MBPP Go 45.6 38.9 43.3 46.2 53.2
BC MBPP Java 41.8 49.7 50.3 57.3 62.9
BC MBPP JavaScript 45.3 45.0 58.2 61.4 61.4
BC MBPP Kotlin 46.8 49.7 54.7 59.9 62.6
BC MBPP Python 38.6 52.9 59.1 62.0 60.2
BC MBPP Rust 45.3 47.4 52.9 53.5 52.3

Natural Language Benchmarks

CodeGemma Natural Language Benchmarks

Downloads last month
98
GGUF
Model size
8.54B params
Architecture
gemma
+1
Inference Examples
Inference API (serverless) does not yet support llama.cpp models for this pipeline type.

Collection including google/codegemma-1.1-7b-it-GGUF