[whatmodel.sh](../../README.md) / Apertus 8B Instruct (2509)
# Apertus 8B Instruct (2509)
Swiss AI · released 2025-08 · 8.05B params · apache-2.0
**score 39.9** ±3.4 · #570 of 732 · from 5 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 Apertus 8B Instruct (2509) among every model on that task or test, worst to best.
task vs the others rank counts tests
math ▁▁●▅█▆▄█▄▂▁▁ #387 of 431* 43% 2
math · #387 of 431 · 43% 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
MATH (subset unspecified) 21.9% ●█····██·███ beats 0/6 easy · avg 70% 27% at its level · noisy
OMEGA (math generalisation) 5.0% ●···▄·▄·▄··█ beats 0/6 hard · avg 19% 17% typical
other tests (3)
test result vs the others in its run difficulty counts why
EuroEval Life in the UK (English … 80.3% ▁▁▁▁▁▁▁▂▄●▄▃ beats 68/146 easy · avg 82% 20% at its level · noisy
EuroEval HellaSwag (English), tes… 58.1% ▃·▁▁▂▁▂●█▇▄▁ beats 31/100 medium · avg 67% 20% sharp · at its level · noisy
Icelandic LLM Leaderboard average… 7.7% ●▁▃▂▂▄▁▃▄▄▄█ beats 0/38 very hard · avg 14% 12% too hard for it
source results share of the score
allenai/Olmo-3-7B-Instruct model card 2 43% ▇▇▇▇▇
EuroEval leaderboards 2 40% ▇▇▇▇▇
Miðeind Icelandic LLM Leaderboard 1 12% ▇
prior: pull toward the middle 5% ▇
weighted average = 39.9
Licences: 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)) · Miðeind Icelandic LLM Leaderboard ([CC BY 4.0](../../licenses/mideind-icelandic-llm-leaderboard-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 Apertus 8B Instruct (2509) (Q4_K_M, needs ~5.1 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/Apertus-8B-Instruct-2509-GGUF/tree/61c32783830424ac7a6d0fd80d4166e29dbaee06))
```sh
llama cli -hf unsloth/Apertus-8B-Instruct-2509-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf unsloth/Apertus-8B-Instruct-2509-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.6 GB) `llama cli -hf unsloth/Apertus-8B-Instruct-2509-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 Apertus 8B Instruct (2509) (4bit, needs ~4.53 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Apertus-8B-Instruct-2509-4bit/tree/c5e6ae2e52c4149f36cb8e47b7ab1489ef885fed))
```sh
lms get https://huggingface.co/mlx-community/Apertus-8B-Instruct-2509-4bit -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q4_K_M (~5.1 GB) `lms get https://huggingface.co/unsloth/Apertus-8B-Instruct-2509-GGUF@Q4_K_M -y` · Q8_0 (~8.6 GB) `lms get https://huggingface.co/unsloth/Apertus-8B-Instruct-2509-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 Apertus 8B Instruct (2509) (Q4_K_M, needs ~5.1 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/Apertus-8B-Instruct-2509-GGUF/tree/61c32783830424ac7a6d0fd80d4166e29dbaee06))
```sh
ollama run hf.co/unsloth/Apertus-8B-Instruct-2509-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.6 GB) `ollama run hf.co/unsloth/Apertus-8B-Instruct-2509-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 Apertus 8B Instruct (2509) (4bit, needs ~4.53 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Apertus-8B-Instruct-2509-4bit/tree/c5e6ae2e52c4149f36cb8e47b7ab1489ef885fed))
```sh
mlx_lm.chat --model mlx-community/Apertus-8B-Instruct-2509-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Apertus-8B-Instruct-2509-4bit
```
### Other ways to run it
1. Run it as published (BF16, needs ~16.11 GB, NVIDIA only, from [huggingface.co/swiss-ai](https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509/tree/b946d40447b2b597999b9c86d44bee0b452c919f))
```sh
vllm serve swiss-ai/Apertus-8B-Instruct-2509
```
Model repository and documentation: [huggingface.co/swiss-ai](https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509)
[interactive version](https://whatmodel.sh/model/apertus-8b-instruct-2509) · [JSON](https://whatmodel.sh/api?format=json&all=1)