[whatmodel.sh](../../README.md) / Eurus-2-7B-SFT # Eurus-2-7B-SFT PRIME-RL · released 2024-12 · 7.62B params · apache-2.0 **score 36.3** ±3.0 · #617 of 732 · from 3 benchmark results across 1 source · † no independently run results verified Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Eurus-2-7B-SFT among every model on that task or test, worst to best.}

task  vs the others     rank  counts  tests
math  ●··········█   #2 of 2     96%      3

math · #2 of 2 · 96% of its score

test                  result  vs the others in its run  difficulty           counts  why
AIME 2024 (maker)       3.3%  ▁▁●▂▁▃▁▅███▃ beats 5/85   medium · avg 39%        35%  sharp · small run
MATH-500 (maker)       65.1%  ▁▃▂●▄▇▅▇█▄▂▁ beats 12/85  very easy · avg 86%     34%  at its level · small run
Minerva MATH (maker)   32.7%  ▁·▃▁▁▂▁●▄▁▃▃ beats 12/27  medium · avg 55%        27%  small run

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md)

source                                    results  share of the score
PRIME-RL Eurus-2-7B-PRIME model card (s…        3   96% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        4% ▇
weighted average                                   = 36.3

## 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 Eurus-2-7B-SFT (Q4_K_M, needs ~4.7 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Eurus-2-7B-SFT-GGUF/tree/5cbd9868d3e16c86db4a3ddcbeeec9b1a42dd052)) ```sh 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) ```sh llama serve -hf mradermacher/Eurus-2-7B-SFT-GGUF:Q4_K_M ``` Other versions: Q8_0 (~8.1 GB) `llama cli -hf mradermacher/Eurus-2-7B-SFT-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 Eurus-2-7B-SFT (Q4_K_M, needs ~4.7 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Eurus-2-7B-SFT-GGUF/tree/5cbd9868d3e16c86db4a3ddcbeeec9b1a42dd052)) ```sh lms get https://huggingface.co/mradermacher/Eurus-2-7B-SFT-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 (~8.1 GB) `lms get https://huggingface.co/mradermacher/Eurus-2-7B-SFT-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 Eurus-2-7B-SFT (Q4_K_M, needs ~4.7 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Eurus-2-7B-SFT-GGUF/tree/5cbd9868d3e16c86db4a3ddcbeeec9b1a42dd052)) ```sh ollama run hf.co/mradermacher/Eurus-2-7B-SFT-GGUF:Q4_K_M ``` Other versions: Q8_0 (~8.1 GB) `ollama run hf.co/mradermacher/Eurus-2-7B-SFT-GGUF:Q8_0` Model repository and documentation: [huggingface.co/PRIME-RL](https://huggingface.co/PRIME-RL/Eurus-2-7B-SFT) [interactive version](https://whatmodel.sh/model/eurus-2-7b-sft) · [JSON](https://whatmodel.sh/api?format=json&all=1)