What are the hardware requirements for Codegemma 7B It?

Codegemma 7B It requires approximately 17.1 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Codegemma 7B It and how large is it?

Codegemma 7B It is developed by google, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Codegemma 7B It perform on standard benchmarks?

Codegemma 7B It has the following published scores: MMLU 56.1, HumanEval (Python) 52.74. Compare against reference models on this page or on the LLM Explorer leaderboards.

Codegemma 7B It by google — VRAM 17.1GB, 8K context

Name: Codegemma 7B It
Author: google

Codegemma 7B It is an open-source language model by google. Features: 7b LLM, VRAM: 17.1GB, Context: 8K, License: gemma, LLM Explorer Score: 0.19, Arc: 53.8, HellaSwag: 72.5, MMLU: 56.1, GSM8K: 52.5, HumanEval: 52.7.

Conversational Endpoints compatible Gemma Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/google/codegemma-7b-it

Codegemma 7B It Benchmarks

ARC: 53.84 vs 96.7 (so35)^-44.3%

HellaSwag: 72.51 vs 95.3 (gpt4)^-23.9%

MMLU: 56.1 vs 88.3 (so35)^-36.5%

GSM8K: 52.46 vs 96.4 (so35)^-45.6%

HumanEvalA: 27.76

Python Eva: 52.74 vs 92 (so35)^-42.7%

Java Eval: 41.42

Javascript: 47.71

CPP Eval: 42.6

PHP Eval: 19.18

Julia Eval: 19.56

LLME Score: 0.19182

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Codegemma 7B It (google/codegemma-7b-it)

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Codegemma 7B It Parameters and Internals

Model Type

text-to-text, text-to-code, decoder-only

Use Cases

Primary Use Cases:

code completion, code generation, code conversation, code education

Limitations:

large language models have limitations based on training data

Training Details

Data Sources:

publicly available code repositories, open source mathematics datasets, synthetically generated code

Data Volume:

500 billion tokens

Hardware Used:

TPUv5e

Safety Evaluation

Methodologies:

structured evaluations, internal red-teaming testing

Risk Categories:

cyber-offence capabilities, autonomous hacking capabilities

Input Output

Input Format:

code prefix or suffix for code completion, natural language text

Accepted Modalities:

text

Output Format:

fill-in-the-middle code completion, code and natural language

LLM Name	Codegemma 7B It
Repository 🤗	https://huggingface.co/google/codegemma-7b-it
Model Size	7b
Required VRAM	17.1 GB
Updated	2026-05-21
Maintainer	google
Model Type	gemma
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 5.0 GB: 3-of-4 2.1 GB: 4-of-4
Model Architecture	GemmaForCausalLM
License	gemma
Context Length	8192
Model Max Length	8192
Transformers Version	4.39.3
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	bfloat16

Quantized Models of the Codegemma 7B It

Model	Likes	Downloads	VRAM
Codegemma 7B It 8bit	6	242	9 GB
Codegemma 7B It 4bit	2	59	5 GB
Codegemma 7B 4bit	1	42	5 GB
Codegemma 7B It GPTQ	1	7	7 GB
CodeGemma 7B GPTQ	0	9	7 GB
Codegemma 7B It GGUF	0	437	3 GB

Best Alternatives to Codegemma 7B It

Best Alternatives	Context / RAM	Downloads	Likes
Kaggle Math Model Gemma V1	12K / 17.1 GB	5	0
Gemma 1.1 7B It	8K / 17.1 GB	8557	275
Gemma 1.1 7B It	8K / 17.1 GB	12	4
Gemma 7B It	8K / 17.1 GB	1663	10
SeaLLM 7B V2.5	8K / 17.1 GB	12476	51
Zephyr 7B Gemma DPO Avg	8K / 17.1 GB	15	0
Zephyr 7B Gemma Rpo Avg	8K / 17.1 GB	7	0
... Codegemma 2 7B It Alpaca V1.3	8K / 17.1 GB	2	1
... 7B Finetuned Sft Navarasa 2.0	8K / 34 GB	1620	23
Zephyr 7B Gemma V0.1	8K / 17.1 GB	78	124

Note: green Score (e.g. "73.2") means that the model is better than google/codegemma-7b-it.

Rank the Codegemma 7B It Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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Release v20260328a

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Codegemma 7B It by google