[whatmodel.sh](../../README.md) / EuroLLM 1.7B Instruct
# EuroLLM 1.7B Instruct
UTTER (EuroLLM) · released 2024-08 · 1.66B params · apache-2.0
**score 29.1** ±3.1 · #671 of 732 · from 3 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 EuroLLM 1.7B Instruct among every model on that task or test, worst to best.
other tests (3)
test result vs the others in its run difficulty counts why
CPTUB average (SpeakLeash, 0-5 ju… 35.3% ▁●▂▂▃█▃▆▅▄▂▃ beats 5/95 medium · avg 65% 67% at its level
Open PT-LLM Leaderboard average (… 30.8% ●▁▁▁▁▁▁▁▂█▅▁ beats 31/276 medium · avg 72% 15% noisy
EuroEval Life in the UK (English … 29.8% ▁▁▁●▁▁▁▂▄█▄▃ beats 5/146 easy · avg 82% 14% at its level · noisy
source results share of the score
SpeakLeash CPTUB 1 67% ▇▇▇▇▇▇▇▇
Open Portuguese LLM Leaderboard 1 15% ▇▇
EuroEval leaderboards 1 14% ▇▇
prior: pull toward the middle 4% ▇
weighted average = 29.1
Licences: SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.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))
## 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 EuroLLM 1.7B Instruct (Q4_K_M, needs ~1.05 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/EuroLLM-1.7B-Instruct-GGUF/tree/2951f08f66429c934c8b01a94347161362430808))
```sh
llama cli -hf mradermacher/EuroLLM-1.7B-Instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/EuroLLM-1.7B-Instruct-GGUF:Q4_K_M
```
### 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 EuroLLM 1.7B Instruct (Q4_K_M, needs ~1.05 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/EuroLLM-1.7B-Instruct-GGUF/tree/2951f08f66429c934c8b01a94347161362430808))
```sh
lms get https://huggingface.co/mradermacher/EuroLLM-1.7B-Instruct-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: 4bit (~0.9 GB, Mac only) `lms get https://huggingface.co/jevoma4362/EuroLLM-1.7B-Instruct-mlx-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 EuroLLM 1.7B Instruct (Q4_K_M, needs ~1.05 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/EuroLLM-1.7B-Instruct-GGUF/tree/2951f08f66429c934c8b01a94347161362430808))
```sh
ollama run hf.co/mradermacher/EuroLLM-1.7B-Instruct-GGUF:Q4_K_M
```
### 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 EuroLLM 1.7B Instruct (4bit, needs ~0.9 GB, Mac only, from [huggingface.co/jevoma4362](https://huggingface.co/jevoma4362/EuroLLM-1.7B-Instruct-mlx-4Bit/tree/6564fac274047a98b97c54287607626553f5647f))
```sh
mlx_lm.chat --model jevoma4362/EuroLLM-1.7B-Instruct-mlx-4Bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model jevoma4362/EuroLLM-1.7B-Instruct-mlx-4Bit
```
### Other ways to run it
1. Run it as published (BF16, needs ~3.31 GB, NVIDIA only, from [huggingface.co/utter-project](https://huggingface.co/utter-project/EuroLLM-1.7B-Instruct/tree/a25c7fa65fc2a644e6270b8940dbe295b51da681))
```sh
vllm serve utter-project/EuroLLM-1.7B-Instruct
```
Model repository and documentation: [huggingface.co/utter-project](https://huggingface.co/utter-project/EuroLLM-1.7B-Instruct)
[interactive version](https://whatmodel.sh/model/eurollm-1-7b-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)