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CodeGemma 7B IT (v1.0)

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CodeGemma 7B IT (v1.0) is an open-weights language model from Google, released March 2024. Its published size is 8.54B parameters. Licence: gemma.

Catalogue identity notes

Maker repo google/codegemma-7b-it @078cdc5: licence tag gemma. 8,537,680,896 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-03: month the maker's Hugging Face repo was created (createdAt 2024-03-21). Instruction-tuned CodeGemma 7B, original release; codegemma-1.1-7b-it is separate. Ollama tags codegemma:7b-instruct-q4_K_M, codegemma:7b-instruct-q8_0 (sizes as listed on the tags page, observed 2026-09-29).

42.5±0.7#494 of 719 LLMsfrom 10 benchmark results across 4 sources

$ how it compares

The LLMs ranked either side of CodeGemma 7B IT (v1.0), out of 828 with a score, then more from Google.

#modelscorewhereRAMreleased
582Aya 23 8B Cohere For AI43±3local~6.2GB2024-05
583Phi-3-small-8k-instruct Microsoft43±2local~17GB2024-05
584CodeGemma 7B IT (v1.0) Google43±1local~7.1GB2024-03
585Aya Vision 32B Cohere42±3api price unknown +local~71GB2025-03
586Tulu 2 DPO 70B Ai242±1local~32GB2023-11
more from Google
9Gemini 4 Argon (limited preview) Google82±1limited preview2026-09
23Gemini 3.8 Flash Google77±1api $1.50/M2026-09
32Gemini 3.7 Flash Google76±1api $1.50/M2026-08

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #350 of 458 · 91% of its score · 7 tests

Beats LFM2.5 2.6B, LFM2.5 8B-A1B, Qwen3-VL 2B Thinking here, though they rank higher overall. Trails Phi-3-mini-128k-instruct (Apr 2024), Code Tulu 2 13B, DeepSeek LLM 67B Chat here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval +160.4% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 46/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 41%sharp · at its level · several runs
HumanEval+51.8% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 45/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇▇ 27%sharp · at its level
HumanEvalSynthesize (HumanEvalPack, 6-language average)41.6% ✎▁·▂▁▂▆▅█▄▄▁▂ beats 21/35medium · avg 64%▇▇▇ 7.1%at its level
HumanEvalExplain (HumanEvalPack, 6-language average)38.4% ✎▄▇█▃▄▇▄▁▇▃·▁ beats 30/35medium · avg 49%▇▇ 4.8%at its level
MBPP+56.9% ✎▁···▁▁▂▅▇█▆▂ beats 33/75easy · avg 68%▇▇ 4.7%at its level
HumanEvalFix (HumanEvalPack, 6-language average)45.8% ✎▁·▁▁▄██▄█▁▅▅ beats 31/35medium · avg 56%▇▇ 4.7%sharp · at its level
MBPP lab54.2% ✎▁▁▁▄▅█▄▅▃▁▂▁ beats 40/81easy · avg 74%▇ 2.1%at its level
other tests2 not in a task · 8.6% of its score
testresultvs the others in its rundifficultycountswhy
BigCodeBench (Complete)38.3% ✎▂▁▁▄▂▅▇█▆▂▂▂ beats 23/58medium · avg 50%▇▇ 4.4%at its level
BigCodeBench (Instruct)32.1% ✎▃▂·▆▆██▆▅▃▃▃ beats 10/33medium · avg 41%▇▇ 4.3%at its level

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run CodeGemma 7B IT (v1.0) · needs ~5.3 GB · from huggingface.co/lmstudio-community
    chat in the terminal
    llama cli -hf lmstudio-community/codegemma-7b-it-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf lmstudio-community/codegemma-7b-it-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run CodeGemma 7B IT (v1.0) · needs ~5.3 GB · from huggingface.co/lmstudio-community

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/lmstudio-community/codegemma-7b-it-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run CodeGemma 7B IT (v1.0) · needs ~5.3 GB · from ollama.com
    chat in the terminal
    ollama run codegemma:7b-instruct-q4_K_M

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run CodeGemma 7B IT (v1.0) · needs ~5.9 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/codegemma-7b-it-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/codegemma-7b-it-4bit

Official weights and documentation: huggingface.co/google ↗

$ how the score was worked out · EvalPlus / EvalPerf published… 67%, Granite Code Models: A Family… 17%, BigCodeBench paper (Zhuo et a… 9% and 1 more

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
EvalPlus / EvalPerf published evaluation runs3▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 67%Apache-2.0
Granite Code Models: A Family of Open Foundation Models for Code Intelligence — authors' own runs3▇▇▇▇▇ 17%CC BY 4.0
BigCodeBench paper (Zhuo et al., ICLR 2025), arXiv 2406.15877v42▇▇▇ 9%CC BY 4.0
CodeGemma: Open Code Models Based on Gemma — authors' own runs · lab-reported2▇▇ 8%CC BY 4.0
prior · pull toward the middle▇ 0%
weighted average= 42.5

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json