[whatmodel.sh](../../README.md) / Gemma 2B IT
# Gemma 2B IT
Google · released 2024-02 · 2.51B params · gemma
**score 28.6** ±0.8 · #674 of 732 · from 12 benchmark results across 9 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Gemma 2B IT among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁●▃▅▇█▇▅▄▃▁ #453 of 469 69% 5
chat & writing ▁▁●▃▆▄▆█▆▇▇▃ #390 of 402 21% 4
coding · #453 of 469 · 69% of its score
Beats [Molmo 7B-D (0924)](molmo-7b-d.md), [Tulu 2 7B](tulu-2-7b.md), [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md) here, though they rank higher overall. Trails [Falcon3 1B Instruct](falcon3-1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 17.7% ▁·▁●▃▄▆▅█▃▁▁ beats 5/99 easy · avg 76% 33% sharp
HumanEval+ 15.2% ▁·▁▁●▃▄▄▄█▃▁ beats 5/99 easy · avg 70% 23% sharp
HumanEvalSynthesize (HumanEvalPac… 11.5% ●·▂▁▂▆▅█▄▄▁▂ beats 0/35 medium · avg 62% 7.9% typical
HumanEvalExplain (HumanEvalPack, … 10.2% ●▇█▃▄▇▄▁▇▃·▁ beats 0/35 medium · avg 46% 3.4% typical
HumanEvalFix (HumanEvalPack, 6-la… 16.7% ▁·▁▁▄█●▄█▁▅▅ beats 16/35 medium · avg 51% 2.2% sharp · too hard for it
chat & writing · #390 of 402 · 21% of its score
Beats [Phi-2 (2.7B)](phi-2.md), [OLMo 7B Instruct](olmo-7b-instruct.md), [Falcon 40B Instruct](falcon-40b-instruct.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [Granite 4.0 350M](granite-4-0-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1093.7 ▁▁●▂▃▃▃▆▆▇█▃ beats 15/385 avg 1260 7.4% blunt
Polyrating rating (bias-aware re-… 995.0 ▂▁▂▁●▇█▆▇▄▂▂ beats 14/95 avg 1187 6.8% typical
AlpacaEval 2 length-controlled 5.4% ▁▂●▃▆▆█▆▄▄▁▁ beats 9/122 medium · avg 35% 5.5% sharp · too hard for it
Arena-Hard (v0.1, GPT-4-1106 judg… 3.0% ●▁▁▅█▅▄▃▃▅▃▁ beats 0/60 medium · avg 47% 1.3% sharp · too hard for it
other tests (3)
test result vs the others in its run difficulty counts why
WildBench v2 WB-Score (task macro) -9.6919 WB-Score ●·▁▁·▃█▄▇▆▅▆ beats 0/52 avg 43.019 WB-Score 7.8% sharp
Open PT-LLM Leaderboard average (… 34.3% ▃▁●▁▁▁▁▁▂█▅▁ beats 40/276 medium · avg 72% 1.1% noisy
EuroEval HellaSwag (English), tes… 27.6% ▃·●▁▂▁▂▅█▇▄▁ beats 10/100 medium · avg 67% 0.5% sharp · noisy
source results share of the score
EvalPlus / EvalPerf published evaluatio… 2 56% ▇▇▇▇▇▇▇
Granite Code Models: A Family of Open F… 3 13% ▇▇
WildBench v2 leaderboard data 1 8% ▇
LMArena leaderboard dataset 1 7% ▇
Polyrating: A Cost-Effective and Bias-A… 1 7% ▇
AlpacaEval 1.0 and 2.0 leaderboards 1 6% ▇
Arena-Hard-Auto v0.1 leaderboard 1 1% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
EuroEval leaderboards 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 28.6
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)) · WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE))
## 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 Gemma 2B IT (Q4_K_M, needs ~1.5 GB, from [huggingface.co/second-state](https://huggingface.co/second-state/Gemma-2b-it-GGUF/tree/5edbf96ca7d2a2cd7b4b83fa8cb1d35ed02a94b8))
```sh
llama cli -hf second-state/Gemma-2b-it-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf second-state/Gemma-2b-it-GGUF:Q4_K_M
```
Other versions: Q8_0 (~2.7 GB) `llama cli -hf second-state/Gemma-2b-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 Gemma 2B IT (Q4_K_M, needs ~1.5 GB, from [huggingface.co/second-state](https://huggingface.co/second-state/Gemma-2b-it-GGUF/tree/5edbf96ca7d2a2cd7b4b83fa8cb1d35ed02a94b8))
```sh
lms get https://huggingface.co/second-state/Gemma-2b-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 (~2.7 GB) `lms get https://huggingface.co/second-state/Gemma-2b-it-GGUF@Q8_0 -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 Gemma 2B IT (Q4_K_M, needs ~2.01 GB, from [ollama.com](https://ollama.com/library/gemma/tags))
```sh
ollama run gemma:2b-instruct-q4_K_M
```
Other versions: Q8_0 (~3.51 GB) `ollama run gemma:2b-instruct-q8_0`
Model repository and documentation: [huggingface.co/google](https://huggingface.co/google/gemma-2b-it)
[interactive version](https://whatmodel.sh/model/gemma-2b) · [JSON](https://whatmodel.sh/api?format=json&all=1)