[whatmodel.sh](../../README.md) / CodeGemma 7B IT (v1.0)
# CodeGemma 7B IT (v1.0)
Google · released 2024-03 · 8.54B params · gemma
**score 44.1** ±0.7 · #497 of 732 · from 10 benchmark results across 4 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks CodeGemma 7B IT (v1.0) among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅●█▇▅▄▃▁ #357 of 469 90% 7
coding · #357 of 469 · 90% of its score
Beats [LFM2.5 2.6B](lfm2-5-2-6b.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [Qwen3-VL 2B Thinking](qwen3-vl-2b-thinking.md) here, though they rank higher overall. Trails [Code Tulu 2 13B](code-tulu-2-13b.md), [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 60.4% ▁·▁▁▃▄▆●█▃▁▁ beats 46/99 easy · avg 76% 39% sharp · at its level · several runs
HumanEval+ 51.8% ▁·▁▁▁▃▄▄●█▃▁ beats 45/99 easy · avg 70% 27% sharp · at its level
HumanEvalSynthesize (HumanEvalPac… 41.6% ▁·▂▁▂▆▅●▄▄▁▂ beats 21/35 medium · avg 62% 7.5% at its level
MBPP+ 56.9% ▁···▁▁▂▅●█▆▂ beats 33/75 easy · avg 66% 5.1% at its level
HumanEvalExplain (HumanEvalPack, … 38.4% ▄▇█▃▄▇▄▁●▃·▁ beats 30/35 medium · avg 46% 5.0% at its level
HumanEvalFix (HumanEvalPack, 6-la… 45.8% ▁·▁▁▄██▄█▁●▅ beats 31/35 medium · avg 51% 4.9% sharp · at its level
MBPP (maker) 54.2% ▁▁▁▄▅●▆▅▄▁▂▁ beats 44/86 easy · avg 71% 2.2% at its level
other tests (2)
test result vs the others in its run difficulty counts why
BigCodeBench (Complete) 38.3% ▂▁▁▄▂▅●█▆▂▂▂ beats 23/58 medium · avg 48% 4.8% at its level
BigCodeBench (Instruct) 32.1% ▃▂·▆●██▆▅▃▃▃ beats 10/33 medium · avg 39% 4.7% at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 65% ▇▇▇▇▇▇▇▇
Granite Code Models: A Family of Open F… 3 17% ▇▇
BigCodeBench paper 2 9% ▇
CodeGemma: Open Code Models Based on Ge… 2 8% ▇
prior: pull toward the middle 0% ▇
weighted average = 44.1
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · Granite Code Models: A Family of Open Foundation Models for Code Intelligence ([CC BY 4.0](../../licenses/paper-2405.04324v1-CC-BY-4.0.txt)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · CodeGemma: Open Code Models Based on Gemma ([CC BY 4.0](../../licenses/paper-2406.11409v2-CC-BY-4.0.txt))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run CodeGemma 7B IT (v1.0) (Q4_K_M, needs ~5.3 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/codegemma-7b-it-GGUF/tree/ffd4dfcc4482d2392160b285a8e2176628e93e7f))
```sh
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)
```sh
llama serve -hf lmstudio-community/codegemma-7b-it-GGUF:Q4_K_M
```
Other versions: Q8_0 (~9.1 GB) `llama cli -hf lmstudio-community/codegemma-7b-it-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run CodeGemma 7B IT (v1.0) (Q4_K_M, needs ~5.3 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/codegemma-7b-it-GGUF/tree/ffd4dfcc4482d2392160b285a8e2176628e93e7f))
```sh
lms get https://huggingface.co/lmstudio-community/codegemma-7b-it-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~9.1 GB) `lms get https://huggingface.co/lmstudio-community/codegemma-7b-it-GGUF@Q8_0 -y` · 4bit (~5.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/codegemma-7b-it-4bit -y` · 8bit (~9.6 GB, Mac only) `lms get https://huggingface.co/mlx-community/codegemma-7b-it-8bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run CodeGemma 7B IT (v1.0) (Q4_K_M, needs ~5.3 GB, from [ollama.com](https://ollama.com/library/codegemma/tags))
```sh
ollama run codegemma:7b-instruct-q4_K_M
```
Other versions: Q8_0 (~9.1 GB) `ollama run codegemma:7b-instruct-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run CodeGemma 7B IT (v1.0) (4bit, needs ~5.9 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/codegemma-7b-it-4bit/tree/59210ff70e9186fcb9de296e6d56e330318af2bf))
```sh
mlx_lm.chat --model mlx-community/codegemma-7b-it-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/codegemma-7b-it-4bit
```
Other versions: 8bit (~9.6 GB, Mac only) `mlx_lm.chat --model mlx-community/codegemma-7b-it-8bit`
Model repository and documentation: [huggingface.co/google](https://huggingface.co/google/codegemma-7b-it)
[interactive version](https://whatmodel.sh/model/codegemma-7b-it) · [JSON](https://whatmodel.sh/api?format=json&all=1)