[whatmodel.sh](../../README.md) / OLMo 2 32B Instruct (0325)
# OLMo 2 32B Instruct (0325)
Ai2 · released 2025-03 · 32.2B params · apache-2.0
**score 50.2** ±1.2 · #367 of 732 · from 14 benchmark results across 5 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks OLMo 2 32B Instruct (0325) among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #277 of 402 27% 3
math ▁▁▃●█▆▄█▄▂▁▁ #339 of 431 19% 3
chat & writing · #277 of 402 · 27% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Ministral 3 14B](ministral-3-14b.md), [Ministral 3 8B](ministral-3-8b.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [LFM2 1.2B](lfm2-1-2b.md), [North Micro Vision Instruct (2.4B)](north-micro-vision-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1251.7 ▁▁▁▂▃▃●▆▆▇█▃ beats 91/385 avg 1260 15% blunt
AlpacaEval 2 length-controlled (m… 42.8% ▃▃·▁▁█●▅·▁·▁ beats 16/22 medium · avg 39% 10% sharp · at its level
IFEval prompt-level loose (maker) 85.6% ▁▃▁▃▁▄▁▁█●▃▁ beats 18/22 easy · avg 78% 2.8% typical
math · #339 of 431 · 19% of its score
Beats [Athene-70B](athene-70b.md), [Llama 3 70B Instruct](llama-3-70b.md) here, though they rank higher overall. Trails [Gemma 2B (base)](gemma-2b-base.md), [Stable Code 3B](stable-code-3b.md), [Zephyr 7B beta](zephyr-7b-beta.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH (subset unspecified) (maker) 49.7% ▃▃█●▂▆·····▁ beats 13/22 medium · avg 58% 8.3% at its level · several runs
GSM8K (maker) 87.6% ▂▃··▃▂▅█●▃▂▃ beats 16/22 very easy · avg 88% 7.5% sharp
OMEGA (math generalisation) (make… 9.8% ●···▃▃···▃▃█ beats 1/8 hard · avg 24% 2.9% typical
other tests (7)
test result vs the others in its run difficulty counts why
MMLU (maker) 77.3% ▁▂·▁·▁▁█·●▃▅ beats 14/22 easy · avg 75% 21% at its level
BIG-Bench Hard (maker) 70.6% ▁▄·▁▁·▅●·▄▄▁ beats 12/22 medium · avg 72% 19% sharp · at its level
PopQA (maker) 37.5% ▁▁▁▃▃▃▁█▁●·▄ beats 17/21 hard · avg 31% 4.4% at its level
Open PT-LLM Leaderboard average (… 74.8% ▃▁▁▁▁▁▁▁▂█●▁ beats 224/276 medium · avg 72% 3.4% at its level · noisy
EuroEval HellaSwag (English), tes… 80.6% ▃·▁▁▂▁▂▅█●▄▁ beats 74/100 medium · avg 67% 2.9% sharp · at its level · noisy
EuroEval Life in the UK (English … 90.8% ▁▁▁▁▁▁▁▂▄█●▃ beats 119/146 easy · avg 82% 1.8% noisy
TruthfulQA MC2 (maker) 73.2% ▃▃▂·▃▃·▅█▂▅● beats 21/21 medium · avg 60% 0.6% blunt · at its level
source results share of the score
2 OLMo 2 Furious (submitted / maker-rep… 8 71% ▇▇▇▇▇▇▇▇
LMArena leaderboard dataset 1 15% ▇▇
allenai/Olmo-3.1-32B-Think model card (… 2 6% ▇
EuroEval leaderboards 2 5% ▇
Open Portuguese LLM Leaderboard 1 3% ▇
prior: pull toward the middle 1% ▇
weighted average = 50.2
Licences: 2 OLMo 2 Furious ([CC BY 4.0](../../licenses/paper-2501.00656v3-CC-BY-4.0.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · allenai/Olmo-3.1-32B-Think model card ([Apache-2.0](../../licenses/allenai--Olmo-3.1-32B-Think.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))
## 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 OLMo 2 32B Instruct (0325) (Q4_K_S, needs ~18.42 GB, from [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-2-0325-32B-Instruct-GGUF/tree/66b39389b9c7839798fb4c9b85c849f194ad8c67))
```sh
llama cli -hf allenai/OLMo-2-0325-32B-Instruct-GGUF:Q4_K_S
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf allenai/OLMo-2-0325-32B-Instruct-GGUF:Q4_K_S
```
Other versions: Q8_0 (~34.26 GB) `llama cli -hf allenai/OLMo-2-0325-32B-Instruct-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 OLMo 2 32B Instruct (0325) (Q4_K_S, needs ~18.42 GB, from [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-2-0325-32B-Instruct-GGUF/tree/66b39389b9c7839798fb4c9b85c849f194ad8c67))
```sh
lms get https://huggingface.co/allenai/OLMo-2-0325-32B-Instruct-GGUF@Q4_K_S -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~34.26 GB) `lms get https://huggingface.co/allenai/OLMo-2-0325-32B-Instruct-GGUF@Q8_0 -y` · 4bit (~18.1 GB, Mac only) `lms get https://huggingface.co/mlx-community/OLMo-2-0325-32B-Instruct-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 OLMo 2 32B Instruct (0325) (Q4_K_S, needs ~18.42 GB, from [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-2-0325-32B-Instruct-GGUF/tree/66b39389b9c7839798fb4c9b85c849f194ad8c67))
```sh
ollama run hf.co/allenai/OLMo-2-0325-32B-Instruct-GGUF:Q4_K_S
```
Other versions: Q8_0 (~34.26 GB) `ollama run hf.co/allenai/OLMo-2-0325-32B-Instruct-GGUF: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 OLMo 2 32B Instruct (0325) (4bit, needs ~18.1 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/OLMo-2-0325-32B-Instruct-4bit/tree/bcf85817c3502e1f974b5abc586f1fc3f81b1632))
```sh
mlx_lm.chat --model mlx-community/OLMo-2-0325-32B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/OLMo-2-0325-32B-Instruct-4bit
```
Model repository and documentation: [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-2-0325-32B-Instruct)
[interactive version](https://whatmodel.sh/model/olmo-2-32b) · [JSON](https://whatmodel.sh/api?format=json&all=1)