[whatmodel.sh](../../README.md) / OLMo 1B
# OLMo 1B
Allen Institute for AI · released 2024-02 · 1.18B params · apache-2.0
**score 20.8** ±1.7 · #699 of 732 · from 8 benchmark results across 1 source · † no independently run results verified
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks OLMo 1B among every model on that task or test, worst to best.
other tests (8)
test result vs the others in its run difficulty counts why
ARC Challenge (maker) 34.5% ▃●······▃▅▃█ beats 1/8 easy · avg 86% 17% sharp
HellaSwag (maker) 62.5% ▂·····●▂·▂·█ beats 1/8 very easy · avg 91% 16% at its level
WinoGrande (maker) 58.9% ▂····●·▂·▂▂█ beats 1/8 easy · avg 86% 16% typical
SciQ (maker) 88.1% ▂·●···▂·▂·█▂ beats 1/8 very easy · avg 98% 13% blunt
OpenBookQA (maker) 46.4% ▄·····▄●▄██▄ beats 2/8 easy · avg 81% 12% typical
ARC Easy (maker) 58.1% ▃···●·▃··▃▅█ beats 1/8 easy · avg 85% 10% blunt · at its level
BoolQ (maker) 60.7% ●····▃··█▃·▃ beats 0/8 easy · avg 88% 8.7% at its level
PIQA (maker) 73.7% ▄····●·▄▄▄██ beats 1/8 easy · avg 86% 6.1% blunt · at its level
source results share of the score
OLMo: Accelerating the Science of Langu… 8 99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 1% ▇
weighted average = 20.8
Licences: OLMo: Accelerating the Science of Language Models ([CC BY 4.0](../../licenses/paper-2402.00838v4-CC-BY-4.0.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 1B (Q4_K_M, needs ~0.7 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/OLMo-1B-hf-GGUF/tree/f889f0919626c42b00d08479df5bd107a698a3a2))
```sh
llama cli -hf mradermacher/OLMo-1B-hf-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/OLMo-1B-hf-GGUF:Q4_K_M
```
Other versions: Q8_0 (~1.3 GB) `llama cli -hf mradermacher/OLMo-1B-hf-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 1B (Q4_K_M, needs ~0.7 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/OLMo-1B-hf-GGUF/tree/f889f0919626c42b00d08479df5bd107a698a3a2))
```sh
lms get https://huggingface.co/mradermacher/OLMo-1B-hf-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 (~1.3 GB) `lms get https://huggingface.co/mradermacher/OLMo-1B-hf-GGUF@Q8_0 -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 1B (Q4_K_M, needs ~0.7 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/OLMo-1B-hf-GGUF/tree/f889f0919626c42b00d08479df5bd107a698a3a2))
```sh
ollama run hf.co/mradermacher/OLMo-1B-hf-GGUF:Q4_K_M
```
Other versions: Q8_0 (~1.3 GB) `ollama run hf.co/mradermacher/OLMo-1B-hf-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (fp32, needs ~4.71 GB, from [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-1B-hf/tree/aee7752d9c08ee4775e9b0091426d8410e8f6a89))
```sh
uv run --with torch --with 'transformers<5' python -c "from transformers import pipeline; print(pipeline('text-generation', model='allenai/OLMo-1B-hf')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/allenai](https://huggingface.co/allenai/OLMo-1B-hf)
[interactive version](https://whatmodel.sh/model/olmo-1b) · [JSON](https://whatmodel.sh/api?format=json&all=1)