whatmodel.sh / Eurus-2-7B-SFT

Eurus-2-7B-SFT is an open-weights language model from PRIME-RL, released December 2024. Its published size is 7.62B parameters. Licence: apache-2.0.

Catalogue identity notes

Comparison-only, proposed so the Eurus-2-7B-PRIME card table counts. The PRIME card compares Eurus-2-7B-PRIME with 'Eurus-2-7B-SFT', the PRIME team's own SFT starting checkpoint. Weights: https://huggingface.co/PRIME-RL/Eurus-2-7B-SFT (sha 2f38b9435f3a99d8899d4f00df8b6ebf0b3f502e, created 2024-12-30, apache-2.0, 7,615,616,512 parameters in safetensors metadata).

34.9±3.0#601 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of Eurus-2-7B-SFT, out of 828 with a score, then more from PRIME-RL.

#modelscorewhereRAMreleased
694Gemma 7B (base) Google35±1local~7.1GB2024-02
695Tulu 2 DPO 7B Allen Institute for AI35±1local~5.8GB2023-11
696Eurus-2-7B-SFT PRIME-RL35±3local~6.4GB2024-12
697Falcon 40B (base) TII35±1local~27GB2023-05
698StarCoder2 7B BigCode35±1local~6.1GB2024-02
more from PRIME-RL
545Eurus-2-7B-PRIME PRIME-RL44±2local~6.4GB2024-12

see all LLMs · find the ones that fit your machine

$ what it's good at

math█··········█ #2 of 2 · 96% of its score · 3 tests
testresultvs the others in its rundifficultycountswhy
MATH-500 lab65.1% ✎▁▃▂▃▅▇▅▇█▅▂▁ beats 12/84very easy · avg 87%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 37%at its level · small run
AIME 2024 lab3.3% ✎▁▁▃▂▁▃▁▅███▃ beats 5/85medium · avg 42%▇▇▇▇▇▇▇▇▇▇▇▇▇ 33%sharp · small run
Minerva MATH lab32.7% ✎▄·▂▂·██▆·▆▄▄ beats 7/21medium · avg 59%▇▇▇▇▇▇▇▇▇▇ 26%at its level · small run

$ where to run it

llama.cpp · recommended

version

  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 Eurus-2-7B-SFT · needs ~4.7 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/Eurus-2-7B-SFT-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/Eurus-2-7B-SFT-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 Eurus-2-7B-SFT · needs ~4.7 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/Eurus-2-7B-SFT-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Eurus-2-7B-SFT · needs ~4.7 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/Eurus-2-7B-SFT-GGUF:Q4_K_M

Official weights and documentation: huggingface.co/PRIME-RL ↗

$ how the score was worked out · PRIME-RL Eurus-2-7B-PRIME mod… 96%

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
PRIME-RL Eurus-2-7B-PRIME model card, Evaluation table (own models) · lab-reported3▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 96%Factual vendor-reported score (not licensed content)
prior · pull toward the middle▇ 4%
weighted average= 34.9

$ improve this page

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