[whatmodel.sh](../../README.md) / OLMo 2 7B Instruct (1124)
# OLMo 2 7B Instruct (1124)
Allen Institute for AI · released 2024-12 · 7.3B params · apache-2.0
**score 43.1** ±1.2 · #516 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 7B Instruct (1124) among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃▆●▆█▆▇▇▃ #294 of 402* 11% 2
math ▁▁▃●█▆▄█▄▂▁▁ #367 of 431 26% 3
chat & writing · #294 of 402 · 11% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Gemma 4 E4B](gemma-4-e4b.md), [OpenThinker3 7B](openthinker3-7b.md) here, though they rank higher overall. Trails [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [LFM2 700M](lfm2-700m.md), [Flan-T5 XXL](flan-t5-xxl.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled (m… 29.1% ▃▃·▁▁●▇▅·▁·▁ beats 6/22 medium · avg 39% 7.6% sharp
IFEval prompt-level loose (maker) 72.3% ▁▃▁▃▁●▁▁█▃▃▁ beats 9/22 easy · avg 78% 3.7% at its level
math · #367 of 431 · 26% of its score
Beats [GPT-3.5 Turbo (0125)](gpt-3-5-turbo-0125.md), [DBRX Instruct](dbrx-instruct.md), [Command R Plus (04-2024)](command-r-plus-04-2024.md) here, though they rank higher overall. Trails [OPT-1.3B](opt-1-3b.md), [OPT-2.7B](opt-2-7b.md), [Gemma 2B (base)](gemma-2b-base.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 85.1% ▂▃··▃▂▅●▆▃▂▃ beats 13/22 very easy · avg 88% 14% sharp · at its level
MATH (subset unspecified) (maker) 32.5% ▃●█▂▂▆·····▁ beats 4/22 medium · avg 58% 9.2% at its level · several runs
OMEGA (math generalisation) (make… 5.2% ●···▄·▄·▄··█ beats 1/6 hard · avg 19% 2.7% typical
other tests (8)
test result vs the others in its run difficulty counts why
MMLU (maker) 61.3% ▁▂·▁·▁●█·▃▃▅ beats 5/22 easy · avg 75% 26% at its level
BIG-Bench Hard (maker) 51.4% ▁▄·▁●·▅█·▄▄▁ beats 5/22 medium · avg 72% 21% sharp · at its level
PopQA (maker) 23.2% ▁▁▁▃▃●▁█▁▃·▄ beats 8/21 hard · avg 31% 3.8% typical
Open PT-LLM Leaderboard average (… 63.7% ▃▁▁▁▁▁▁▁●█▅▁ beats 105/276 medium · avg 72% 3.7% at its level · noisy
EuroEval HellaSwag (English), tes… 64.1% ▃·▁▁▂▁▂●█▇▄▁ beats 38/100 medium · avg 67% 2.9% sharp · at its level · noisy
EuroEval Life in the UK (English … 79.3% ▁▁▁▁▁▁▁▂▄●▄▃ beats 62/146 easy · avg 82% 2.5% at its level · noisy
Vectara hallucination leaderboard… 88.9% ▁▁▂●▄▄█▆▃▂▂▁ beats 21/140 very easy · avg 94% 1.4% blunt · noisy
TruthfulQA MC2 (maker) 56.5% ▃▃▂·▃●·▅█▂▅▂ beats 8/21 medium · avg 60% 0.7% blunt · at its level
source results share of the score
2 OLMo 2 Furious (submitted / maker-rep… 8 83% ▇▇▇▇▇▇▇▇▇▇
allenai/Olmo-3-7B-Instruct model card (… 2 6% ▇
EuroEval leaderboards 2 5% ▇
Open Portuguese LLM Leaderboard 1 4% ▇
Vectara hallucination leaderboard 1 1% ▇
prior: pull toward the middle 1% ▇
weighted average = 43.1
Licences: 2 OLMo 2 Furious ([CC BY 4.0](../../licenses/paper-2501.00656v3-CC-BY-4.0.txt)) · allenai/Olmo-3-7B-Instruct model card ([Apache-2.0](../../licenses/allenai--Olmo-3-7B-Instruct.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)) · 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 7B Instruct (1124) (Q4_K_M, needs ~4.5 GB, from [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-2-1124-7B-Instruct-GGUF/tree/410e0069f64869e4b1d17d8de04810b881fd824b))
```sh
llama cli -hf allenai/OLMo-2-1124-7B-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-2-1124-7B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.8 GB) `llama cli -hf allenai/OLMo-2-1124-7B-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 7B Instruct (1124) (Q4_K_M, needs ~4.5 GB, from [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-2-1124-7B-Instruct-GGUF/tree/410e0069f64869e4b1d17d8de04810b881fd824b))
```sh
lms get https://huggingface.co/allenai/OLMo-2-1124-7B-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 (~7.8 GB) `lms get https://huggingface.co/allenai/OLMo-2-1124-7B-Instruct-GGUF@Q8_0 -y` · 4bit (~4.1 GB, Mac only) `lms get https://huggingface.co/mlx-community/OLMo-2-1124-7B-Instruct-4bit -y` · 8bit (~7.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/OLMo-2-1124-7B-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 7B Instruct (1124) (Q4_K_M, needs ~4.88 GB, from [ollama.com](https://ollama.com/library/olmo2/tags))
```sh
ollama run olmo2:7b-1124-instruct-q4_K_M
```
Other versions: Q8_0 (~9.26 GB) `ollama run olmo2:7b-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 7B Instruct (1124) (4bit, needs ~4.1 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/OLMo-2-1124-7B-Instruct-4bit/tree/d755f57773674e695b9eaca1a565160ff10099d4))
```sh
mlx_lm.chat --model mlx-community/OLMo-2-1124-7B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/OLMo-2-1124-7B-Instruct-4bit
```
Other versions: 8bit (~7.8 GB, Mac only) `mlx_lm.chat --model mlx-community/OLMo-2-1124-7B-Instruct-8bit`
Model repository and documentation: [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-2-1124-7B-Instruct)
[interactive version](https://whatmodel.sh/model/olmo-2-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)