[whatmodel.sh](../../README.md) / OLMo 2 13B Instruct (1124)
# OLMo 2 13B Instruct (1124)
Allen Institute for AI · released 2024-12 · 13.72B params · apache-2.0
**score 47.5** ±1.3 · #418 of 732 · from 11 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 OLMo 2 13B Instruct (1124) among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #254 of 402* 15% 2
math ▁▁▃●█▆▄█▄▂▁▁ #346 of 431* 19% 2
chat & writing · #254 of 402 · 15% of its score
Beats [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Qwen2.5 Coder 32B Instruct](qwen2-5-coder-32b.md), [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [Granite 4.0 H 1B](granite-4-0-h-1b.md), [Granite 4.0 1B](granite-4-0-1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled (m… 39.5% ▃▃·▁▁█●▅·▁·▁ beats 15/22 medium · avg 39% 11% sharp · at its level
IFEval prompt-level loose (maker) 82.6% ▁▃▁▃▁▄▁▁●▃▃▁ beats 15/22 easy · avg 78% 3.8% typical
math · #346 of 431 · 19% of its score
Beats [Athene-70B](athene-70b.md), [Gemma 2 9B IT](gemma-2-9b.md), [GPT-3.5 Turbo (0125)](gpt-3-5-turbo-0125.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
GSM8K (maker) 87.4% ▂▃··▃▂▅█●▃▂▃ beats 14/22 very easy · avg 88% 12% sharp
MATH (subset unspecified) (maker) 39.2% ▃▃●▂▂▆·····▁ beats 6/22 medium · avg 58% 6.8% at its level
other tests (7)
test result vs the others in its run difficulty counts why
MMLU (maker) 68.5% ▁▂·▁·▁▁●·▃▃▅ beats 7/22 easy · avg 75% 28% at its level
BIG-Bench Hard (maker) 63.0% ▁▄·▁▁·●█·▄▄▁ beats 6/22 medium · avg 72% 25% sharp · at its level
PopQA (maker) 28.8% ▁▁▁▃▃▃▁●▁▃·▄ beats 12/21 hard · avg 31% 5.0% at its level
EuroEval HellaSwag (English), tes… 68.5% ▃·▁▁▂▁▂▅●▇▄▁ beats 45/100 medium · avg 67% 3.6% sharp · at its level · noisy
EuroEval Life in the UK (English … 83.0% ▁▁▁▁▁▁▁▂▄●▄▃ beats 84/146 easy · avg 82% 2.5% at its level · noisy
Vectara hallucination leaderboard… 89.2% ▁▁▂●▄▄█▆▃▂▂▁ beats 22/140 very easy · avg 94% 1.3% blunt · maxed out · noisy
TruthfulQA MC2 (maker) 64.3% ▃▃▂·▃▃·▅●▂▅▂ beats 13/21 medium · avg 60% 0.7% blunt · at its level
source results share of the score
2 OLMo 2 Furious (submitted / maker-rep… 8 92% ▇▇▇▇▇▇▇▇▇▇▇
EuroEval leaderboards 2 6% ▇
Vectara hallucination leaderboard 1 1% ▇
prior: pull toward the middle 1% ▇
weighted average = 47.5
Licences: 2 OLMo 2 Furious ([CC BY 4.0](../../licenses/paper-2501.00656v3-CC-BY-4.0.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · 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 OLMo 2 13B Instruct (1124) (Q4_K_M, needs ~8.4 GB, from [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct-GGUF/tree/02bfa6da698648c2472c57727cd0098049fe3ec8))
```sh
llama cli -hf allenai/OLMo-2-1124-13B-Instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf allenai/OLMo-2-1124-13B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~14.6 GB) `llama cli -hf allenai/OLMo-2-1124-13B-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 13B Instruct (1124) (Q4_K_M, needs ~8.4 GB, from [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct-GGUF/tree/02bfa6da698648c2472c57727cd0098049fe3ec8))
```sh
lms get https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct-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 (~14.6 GB) `lms get https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct-GGUF@Q8_0 -y` · 4bit (~7.7 GB, Mac only) `lms get https://huggingface.co/mlx-community/OLMo-2-1124-13B-Instruct-4bit -y` · 8bit (~14.6 GB, Mac only) `lms get https://huggingface.co/mlx-community/OLMo-2-1124-13B-Instruct-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 OLMo 2 13B Instruct (1124) (Q4_K_M, needs ~8.73 GB, from [ollama.com](https://ollama.com/library/olmo2/tags))
```sh
ollama run olmo2:13b-1124-instruct-q4_K_M
```
Other versions: Q8_0 (~16.96 GB) `ollama run olmo2:13b-1124-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 OLMo 2 13B Instruct (1124) (4bit, needs ~7.7 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/OLMo-2-1124-13B-Instruct-4bit/tree/2560d915126bdc6e6c4171e8f4f98346a9451309))
```sh
mlx_lm.chat --model mlx-community/OLMo-2-1124-13B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/OLMo-2-1124-13B-Instruct-4bit
```
Other versions: 8bit (~14.6 GB, Mac only) `mlx_lm.chat --model mlx-community/OLMo-2-1124-13B-Instruct-8bit`
Model repository and documentation: [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct)
[interactive version](https://whatmodel.sh/model/olmo-2-13b) · [JSON](https://whatmodel.sh/api?format=json&all=1)