[whatmodel.sh](../../README.md) / Gemma 3 270M IT # Gemma 3 270M IT Google · released 2025-08 · 0.27B params · gemma **score 16.3** ±3.6 · #708 of 732 · from 5 benchmark results across 3 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Gemma 3 270M IT among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
chat & writing  ▁▁▁▃●▄▆█▆▇▇▃   #347 of 402*     44%      1
chat & writing · #347 of 402 · 44% of its score Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [QwQ-32B-Preview](qwq-32b-preview.md) here, though they rank higher overall. 
test                                result  vs the others in its run   difficulty           counts  why
IFEval (aggregation unspecified) …   51.2%  ▁▂●▂▂▄▆██▅▂▁ beats 11/145  very easy · avg 88%     44%  at its level
other tests (8) 
test                                result  vs the others in its run   difficulty           counts  why
BIG-Bench Hard (maker)               26.7%  ▁●▁▁▂▄▅▆█▃▃▁ beats 3/155   easy · avg 79%          30%  sharp · too hard for it
EuroEval Life in the UK (English …   26.7%  ▁▁●▁▁▁▁▂▄█▄▃ beats 4/146   easy · avg 82%         8.9%  noisy
Open PT-LLM Leaderboard average (…   28.2%  ●▁▁▁▁▁▁▁▂█▅▁ beats 37/276  medium · avg 72%       8.7%  too hard for it · noisy
EuroEval HellaSwag (English), tes…   25.2%  ●·▁▁▂▁▂▅█▇▄▁ beats 1/100   medium · avg 67%       2.7%  sharp · too hard for it · noisy
ARC Challenge (maker)                28.2%  ▄▁▁●▁▄█▇▃▂▃▂ beats 23/140  easy · avg 82%            —  not counted yet: no other model in the …
HellaSwag (maker)                    37.7%  ▁▁▁●▂▂▄▆█▇▄▂ beats 11/135  very easy · avg 91%       —  not counted yet: no other model in the …
WinoGrande (maker)                   52.3%  ▁▁▁▁●▁▁▅▇█▃▁ beats 11/131  easy · avg 86%            —  not counted yet: no other model in the …
PIQA (maker)                         66.2%  ▁▁▁●▂▃▅█▅▃▁▁ beats 10/123  easy · avg 87%            —  not counted yet: no other model in the …
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
Google DeepMind Gemma 3 270M IT model c…        2   74% ▇▇▇▇▇▇▇▇▇
EuroEval leaderboards                           2   12% ▇
Open Portuguese LLM Leaderboard                 1    9% ▇
prior: pull toward the middle                        6% ▇
weighted average                                   = 16.3
Licences: Google DeepMind Gemma 3 270M IT model card ([CC-BY-4.0](../../licenses/cards-final-NOTICE.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))
Not counted (4) 
source                  test           score  why
Google DeepMind Gemma…  HellaSwag      37.7%  too few models have it yet
Google DeepMind Gemma…  PIQA           66.2%  too few models have it yet
Google DeepMind Gemma…  ARC Challenge  28.2%  too few models have it yet
Google DeepMind Gemma…  WinoGrande     52.3%  too few models have it yet
## 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 3 270M IT (Q4_K_M, needs ~0.3 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/gemma-3-270m-it-GGUF/tree/2a3f11cd1f383dc6fa7d34c34aef9e44d5bdedf2)) ```sh llama cli -hf lmstudio-community/gemma-3-270m-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-3-270m-it-GGUF:Q4_K_M ``` Other versions: Q8_0 (~0.3 GB) `llama cli -hf lmstudio-community/gemma-3-270m-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 3 270M IT (Q4_K_M, needs ~0.3 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/gemma-3-270m-it-GGUF/tree/2a3f11cd1f383dc6fa7d34c34aef9e44d5bdedf2)) ```sh lms get https://huggingface.co/lmstudio-community/gemma-3-270m-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 (~0.3 GB) `lms get https://huggingface.co/lmstudio-community/gemma-3-270m-it-GGUF@Q8_0 -y` · 4bit (~0.2 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/gemma-3-270m-it-MLX-4bit -y` · 8bit (~0.5 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/gemma-3-270m-it-MLX-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 Gemma 3 270M IT (Q8_0, needs ~0.82 GB, from [ollama.com](https://ollama.com/library/gemma3/tags)) ```sh ollama run gemma3:270m ``` Other versions: qat (~0.66 GB) `ollama run gemma3:270m-it-qat` ### 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 3 270M IT (4bit, needs ~0.2 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/gemma-3-270m-it-MLX-4bit/tree/99d11aebe39a4437f97e21a5216bd1eb3b8f7607)) ```sh mlx_lm.chat --model lmstudio-community/gemma-3-270m-it-MLX-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model lmstudio-community/gemma-3-270m-it-MLX-4bit ``` Other versions: 8bit (~0.5 GB, Mac only) `mlx_lm.chat --model lmstudio-community/gemma-3-270m-it-MLX-8bit` Model repository and documentation: [huggingface.co/google](https://huggingface.co/google/gemma-3-270m-it) [interactive version](https://whatmodel.sh/model/gemma-3-270m) · [JSON](https://whatmodel.sh/api?format=json&all=1)