[whatmodel.sh](../../README.md) / Aya Expanse 8B
# Aya Expanse 8B
Cohere · released 2024-10 · 8.03B params · cc-by-nc-4.0
**score 47.8** ±1.9 · #413 of 732 · from 7 benchmark results across 6 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Aya Expanse 8B among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃▆●▆█▆▇▇▃ #297 of 402* 65% 2
chat & writing · #297 of 402 · 65% 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
LMArena (text) 1223.1 ▁▁▁▂▃●▃▆▆▇█▃ beats 78/385 avg 1260 36% blunt
Arabic IFEval prompt-level (Incep… 48.5% ▁·▁▁▂▁▄▄▆▄●▄ beats 25/38 medium · avg 46% 29% sharp · at its level
other tests (5)
test result vs the others in its run difficulty counts why
Open Persian LLM Leaderboard v2 a… 53.6% ▂▄▂▃█●▃▆▃▅▂▂ beats 39/81 medium · avg 57% 17% blunt · at its level
Open PT-LLM Leaderboard average (… 70.7% ▃▁▁▁▁▁▁▁▂●▅▁ beats 171/276 medium · avg 72% 8.7% at its level · noisy
EuroEval Life in the UK (English … 76.7% ▁▁▁▁▁▁▁▂●█▄▃ beats 55/146 easy · avg 82% 4.9% at its level · noisy
Vectara hallucination leaderboard… 87.8% ▁▁▂●▄▄█▆▃▂▂▁ beats 16/140 very easy · avg 94% 2.6% blunt · maxed out · noisy
Vectara hallucination leaderboard… 90.5% ▁▂▁▂▅●▂▃▄▃▁▁ beats 50/98 very easy · avg 90% 0.0% blunt
source results share of the score
LMArena leaderboard dataset 1 36% ▇▇▇▇
Inception Arabic/Hindi leaderboards 1 29% ▇▇▇
Open Persian LLM Leaderboard v2 1 17% ▇▇
Open Portuguese LLM Leaderboard 1 9% ▇
EuroEval leaderboards 1 5% ▇
Vectara hallucination leaderboard 2 3% ▇
prior: pull toward the middle 2% ▇
weighted average = 47.8
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.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 Aya Expanse 8B (Q4_K_M, needs ~5.1 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/aya-expanse-8b-GGUF/tree/6bf08537b77d7173976591117800a49fe95d9e1d))
```sh
llama cli -hf lmstudio-community/aya-expanse-8b-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/aya-expanse-8b-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.5 GB) `llama cli -hf lmstudio-community/aya-expanse-8b-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 Aya Expanse 8B (Q4_K_M, needs ~5.1 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/aya-expanse-8b-GGUF/tree/6bf08537b77d7173976591117800a49fe95d9e1d))
```sh
lms get https://huggingface.co/lmstudio-community/aya-expanse-8b-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 (~8.5 GB) `lms get https://huggingface.co/lmstudio-community/aya-expanse-8b-GGUF@Q8_0 -y` · 4bit (~4.5 GB, Mac only) `lms get https://huggingface.co/mlx-community/aya-expanse-8b-4bit -y` · 8bit (~8.5 GB, Mac only) `lms get https://huggingface.co/mlx-community/aya-expanse-8b-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 Aya Expanse 8B (Q4_K_M, needs ~5.32 GB, from [ollama.com](https://ollama.com/library/aya-expanse/tags))
```sh
ollama run aya-expanse:8b-q4_K_M
```
Other versions: Q8_0 (~10.14 GB) `ollama run aya-expanse:8b-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 Aya Expanse 8B (4bit, needs ~4.5 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/aya-expanse-8b-4bit/tree/3a7cee37dd4ab4ed76642f61f6253da4748e63ba))
```sh
mlx_lm.chat --model mlx-community/aya-expanse-8b-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/aya-expanse-8b-4bit
```
Other versions: 8bit (~8.5 GB, Mac only) `mlx_lm.chat --model mlx-community/aya-expanse-8b-8bit`
Model repository and documentation: [huggingface.co/CohereLabs](https://huggingface.co/CohereLabs/aya-expanse-8b)
[interactive version](https://whatmodel.sh/model/aya-expanse-8b) · [JSON](https://whatmodel.sh/api?format=json&all=1)