whatmodel.sh / EuroLLM 1.7B Instruct

EuroLLM 1.7B Instruct

+ contribute
Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

EuroLLM 1.7B Instruct is an open-weights language model from UTTER (EuroLLM), released August 2024. Its published size is 1.66B parameters and a 4k-token context window. Licence: apache-2.0.

Catalogue identity notes

utter-project/EuroLLM-1.7B-Instruct @a25c7fa (created 2024-08-06, apache-2.0). UTTER/EuroLLM project (Unbabel, IST Lisbon, Edinburgh and partners) 1.7B instruction model covering the 24 EU languages. Independent rows: EuroEval English, Open PT-LLM, SpeakLeash CPTUB.

Model information sources

Release date ↗ · Weights licence ↗

HF safetensors metadata 1,656,850,432 parameters.

26.6±3.4#665 of 719 LLMsfrom 3 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of EuroLLM 1.7B Instruct, out of 828 with a score, then more from UTTER (EuroLLM).

#modelscorewhereRAMreleased
765MPT-7B-Chat MosaicML27±2comparison only2023-05
766Gemma 2B IT Google27±1local~3.1GB2024-02
767EuroLLM 1.7B Instruct UTTER (EuroLLM)27±3local~2.4GB2024-08
768Qwen2.5 0.5B Instruct Alibaba27±2local~1.8GB2024-09
769GPT-3 text-davinci-001 OpenAI26±3comparison only2022-01
more from UTTER (EuroLLM)
487EuroLLM 9B Instruct UTTER (EuroLLM)47±3local~6.9GB2024-11

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests3 not in a task · 95% of its score
testresultvs the others in its rundifficultycountswhy
CPTUB average (SpeakLeash, 0-5 judge scale)35.3% ✎▁▃▂▂▃█▃▆▅▄▂▃ beats 5/95easy · avg 66%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 67%at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)30.8% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 31/276medium · avg 73%▇▇▇▇▇▇ 14%noisy
EuroEval Life in the UK (English knowledge MC), test split, 5-shot, accuracy29.8% ✎▁▁▁▃▁▁▁▂▄█▄▃ beats 5/146easy · avg 83%▇▇▇▇▇▇ 14%at its level · noisy

$ where to run it

llama.cpp · recommended

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run EuroLLM 1.7B Instruct · needs ~1.05 GB · from huggingface.co/mradermacher
    chat in the terminal
    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)
    llama serve -hf mradermacher/EuroLLM-1.7B-Instruct-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run EuroLLM 1.7B Instruct · needs ~1.05 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/EuroLLM-1.7B-Instruct-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run EuroLLM 1.7B Instruct · needs ~1.05 GB · from huggingface.co/mradermacher
    chat in the terminal
    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.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run EuroLLM 1.7B Instruct · needs ~0.9 GB, Mac only · from huggingface.co/jevoma4362
    chat in the terminal
    mlx_lm.chat --model jevoma4362/EuroLLM-1.7B-Instruct-mlx-4Bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model jevoma4362/EuroLLM-1.7B-Instruct-mlx-4Bit

Other ways to run it

  1. Run it as published · needs ~3.31 GB, NVIDIA only · from huggingface.co/utter-project (official)
    vllm serve utter-project/EuroLLM-1.7B-Instruct

Official weights and documentation: huggingface.co/utter-project ↗

$ how the score was worked out · SpeakLeash CPTUB (Complex Pol… 67%, Open Portuguese LLM Leaderboa… 14%, EuroEval leaderboards (result… 14%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
SpeakLeash CPTUB (Complex Polish Text Understanding Benchmark)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 67%MIT
Open Portuguese LLM Leaderboard (archived)1▇▇▇▇ 14%Apache-2.0
EuroEval leaderboards (results collection), English generative leaderboard1▇▇▇▇ 14%MIT
prior · pull toward the middle▇▇ 5%
weighted average= 26.6

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json