[whatmodel.sh](../../README.md) / OLMo 2 1B Instruct (0425)
# OLMo 2 1B Instruct (0425)
Allen Institute for AI · released 2025-04 · 1.48B params · apache-2.0
**score 34.8** ±1.4 · #630 of 732 · from 9 benchmark results across 2 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 1B Instruct (0425) among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #343 of 402* 11% 2
math ▁▁▃●█▆▄█▄▂▁▁ #385 of 431* 30% 2
chat & writing · #343 of 402 · 11% of its score
Beats [OpenThinker3 7B](openthinker3-7b.md), [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md) here, though they rank higher overall. Trails [Granite 4.0 H 350M](granite-4-0-h-350m.md), [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
IFEval prompt-level loose (maker) 70.1% ▁▃▁▃▁●▁▁█▃▃▁ beats 8/22 easy · avg 78% 5.9% at its level
AlpacaEval 2 length-controlled (m… 9.1% ▃●·▁▁█▇▅·▁·▁ beats 2/22 medium · avg 39% 5.5% sharp
math · #385 of 431 · 30% of its score
Beats [Yi 34B Chat](yi-34b-chat.md), [Llama 3 8B Instruct](llama-3-8b.md), [Command R (03-2024)](command-r-03-2024.md) here, though they rank higher overall. Trails [GPT-Neo 125M](gpt-neo-125m.md), [GPT-2 XL (1.5B)](gpt-2-xl.md), [GPT-Neo 1.3B](gpt-neo-1-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 68.3% ▂▃··●▂▅█▆▃▂▃ beats 4/22 very easy · avg 88% 25% sharp · at its level
MATH (subset unspecified) (maker) 20.7% ●▃█▂▂▆·····▁ beats 1/22 medium · avg 58% 5.8% typical
other tests (5)
test result vs the others in its run difficulty counts why
MMLU (maker) 40.0% ▁●·▁·▁▁█·▃▃▅ beats 2/22 easy · avg 75% 29% at its level
BIG-Bench Hard (maker) 35.0% ●▄·▁▁·▅█·▄▄▁ beats 0/22 medium · avg 72% 20% sharp
Open PT-LLM Leaderboard average (… 45.0% ▃▁▁▁▁▁●▁▂█▅▁ beats 60/276 medium · avg 72% 4.2% at its level · noisy
PopQA (maker) 12.9% ▁●▁▃▃▃▁█▁▃·▄ beats 1/21 hard · avg 31% 3.7% typical
TruthfulQA MC2 (maker) 48.7% ▃▃●·▃▃·▅█▂▅▂ beats 4/21 medium · avg 60% 0.9% blunt · at its level
source results share of the score
2 OLMo 2 Furious (submitted / maker-rep… 8 95% ▇▇▇▇▇▇▇▇▇▇▇
Open Portuguese LLM Leaderboard 1 4% ▇
prior: pull toward the middle 1% ▇
weighted average = 34.8
Licences: 2 OLMo 2 Furious ([CC BY 4.0](../../licenses/paper-2501.00656v3-CC-BY-4.0.txt)) · 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 1B Instruct (0425) (Q4_K_M, needs ~0.9 GB, from [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-2-0425-1B-Instruct-GGUF/tree/62f8c199538474c3e33ed5d7e0580abd66686a27))
```sh
llama cli -hf allenai/OLMo-2-0425-1B-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-2-0425-1B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~1.6 GB) `llama cli -hf allenai/OLMo-2-0425-1B-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 1B Instruct (0425) (Q4_K_M, needs ~0.9 GB, from [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-2-0425-1B-Instruct-GGUF/tree/62f8c199538474c3e33ed5d7e0580abd66686a27))
```sh
lms get https://huggingface.co/allenai/OLMo-2-0425-1B-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 (~1.6 GB) `lms get https://huggingface.co/allenai/OLMo-2-0425-1B-Instruct-GGUF@Q8_0 -y` · 4bit (~1.4 GB, Mac only) `lms get https://huggingface.co/ekryski/OLMo-2-0425-1B-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 1B Instruct (0425) (Q4_K_M, needs ~0.9 GB, from [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-2-0425-1B-Instruct-GGUF/tree/62f8c199538474c3e33ed5d7e0580abd66686a27))
```sh
ollama run hf.co/allenai/OLMo-2-0425-1B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~1.6 GB) `ollama run hf.co/allenai/OLMo-2-0425-1B-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 1B Instruct (0425) (4bit, needs ~1.4 GB, Mac only, from [huggingface.co/ekryski](https://huggingface.co/ekryski/OLMo-2-0425-1B-Instruct-4bit/tree/bb6c2410107c9b1d0f50126955378bc0aa2237cb))
```sh
mlx_lm.chat --model ekryski/OLMo-2-0425-1B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model ekryski/OLMo-2-0425-1B-Instruct-4bit
```
### Other ways to run it
1. Run it as published (BF16, needs ~4.05 GB, NVIDIA only, from [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-2-0425-1B-Instruct))
```sh
vllm serve allenai/OLMo-2-0425-1B-Instruct
```
Model repository and documentation: [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-2-0425-1B-Instruct)
[interactive version](https://whatmodel.sh/model/olmo-2-1b) · [JSON](https://whatmodel.sh/api?format=json&all=1)