[whatmodel.sh](../../README.md) / Gemma 1.1 2B IT
# Gemma 1.1 2B IT
Google · released 2024-04 · 2.51B params · gemma
**score 30.7** ±0.9 · #657 of 732 · from 9 benchmark results across 7 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Gemma 1.1 2B IT among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁●▅▇█▇▅▄▃▁ #448 of 469 77% 3
chat & writing ▁▁▁●▆▄▆█▆▇▇▃ #380 of 402 17% 3
coding · #448 of 469 · 77% of its score
Beats [Qwen3.5 2B](qwen3-5-2b.md), [Molmo 7B-D (0924)](molmo-7b-d.md), [Tulu 2 7B](tulu-2-7b.md) here, though they rank higher overall. Trails [Falcon3 1B Instruct](falcon3-1b.md), [Granite 3.0 1B-A400M Instruct](granite-3-0-1b-a400m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 22.6% ▁·▁▁●▄▆▅█▃▁▁ beats 7/99 easy · avg 76% 41% sharp
HumanEval+ 17.7% ▁·▁▁●▃▄▄▄█▃▁ beats 7/99 easy · avg 70% 28% sharp
MBPP+ 23.3% ▁···●▁▂▅▇█▆▂ beats 1/75 easy · avg 66% 8.2% at its level
chat & writing · #380 of 402 · 17% of its score
Beats [Phi-2 (2.7B)](phi-2.md), [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md), [Nous-Hermes-13b](nous-hermes-13b.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) 1116.7 ▁▁▁●▃▃▃▆▆▇█▃ beats 21/385 avg 1260 7.8% blunt
Polyrating rating (bias-aware re-… 1044.0 ▂▁▂▁▃●█▆▇▄▂▂ beats 24/95 avg 1187 7.1% typical
Arena-Hard (v0.1, GPT-4-1106 judg… 3.4% ●▁▁▅█▅▄▃▃▅▃▁ beats 1/60 medium · avg 47% 1.9% sharp · too hard for it
other tests (3)
test result vs the others in its run difficulty counts why
Polish EQ-Bench v2 (SpeakLeash) 16.5% ▁··●▁▂▂▆▅▆█▆ beats 2/70 medium · avg 62% 3.2% typical
Open PT-LLM Leaderboard average (… 35.5% ▃▁▁●▁▁▁▁▂█▅▁ beats 42/276 medium · avg 72% 1.2% noisy
Vectara hallucination leaderboard… 72.2% ●▁▂▃▄▄█▆▃▂▂▁ beats 1/140 very easy · avg 94% 1.1% blunt · noisy
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 77% ▇▇▇▇▇▇▇▇▇
LMArena leaderboard dataset 1 8% ▇
Polyrating: A Cost-Effective and Bias-A… 1 7% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 3% ▇
Arena-Hard-Auto v0.1 leaderboard 1 2% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
Vectara hallucination leaderboard 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 30.7
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · 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/)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · 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)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## 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 1.1 2B IT (Q4_K_M, needs ~1.6 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/gemma-1.1-2b-it-GGUF/tree/af384c981cb855653076859d5b6f71aec011b9c4))
```sh
llama cli -hf lmstudio-community/gemma-1.1-2b-it-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/gemma-1.1-2b-it-GGUF:Q4_K_M
```
Other versions: Q8_0 (~2.7 GB) `llama cli -hf lmstudio-community/gemma-1.1-2b-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 1.1 2B IT (Q4_K_M, needs ~1.6 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/gemma-1.1-2b-it-GGUF/tree/af384c981cb855653076859d5b6f71aec011b9c4))
```sh
lms get https://huggingface.co/lmstudio-community/gemma-1.1-2b-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/lmstudio-community/gemma-1.1-2b-it-GGUF@Q8_0 -y` · 4bit (~2.2 GB, Mac only) `lms get https://huggingface.co/mlx-community/gemma-1.1-2b-it-4bit -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 1.1 2B IT (Q4_K_M, needs ~2.01 GB, from [ollama.com](https://ollama.com/library/gemma/tags))
```sh
ollama run gemma:2b-instruct-v1.1-q4_K_M
```
Other versions: Q8_0 (~3.51 GB) `ollama run gemma:2b-instruct-v1.1-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 Gemma 1.1 2B IT (4bit, needs ~2.2 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/gemma-1.1-2b-it-4bit/tree/8aacbeed4c6ef63f9c39cfe66b67ca722f81400e))
```sh
mlx_lm.chat --model mlx-community/gemma-1.1-2b-it-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/gemma-1.1-2b-it-4bit
```
Model repository and documentation: [huggingface.co/google](https://huggingface.co/google/gemma-1.1-2b-it)
[interactive version](https://whatmodel.sh/model/gemma-1-1-2b) · [JSON](https://whatmodel.sh/api?format=json&all=1)