[whatmodel.sh](../../README.md) / Eurus-2-7B-PRIME # Eurus-2-7B-PRIME PRIME-RL · released 2024-12 · 7.62B params · apache-2.0 **score 45.4** ±2.3 · #464 of 732 · from 4 benchmark results across 2 sources 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-PRIME among every model on that task or test, worst to best.}

task  vs the others     rank  counts  tests
math  █··········●   #1 of 2     62%      3

math · #1 of 2 · 62% of its score

test                  result  vs the others in its run  difficulty           counts  why
AIME 2024 (maker)      26.7%  ▁▁▁▂▁●▁▅███▃ beats 14/85  medium · avg 39%        27%  sharp · at its level · small run
Minerva MATH (maker)   38.6%  ▁·▃▁▁▂▁●▄▁▃▃ beats 16/27  medium · avg 55%        18%  at its level · small run
MATH-500 (maker)       79.2%  ▁▃▂▃●▇▅▇█▄▂▁ beats 21/85  very easy · avg 86%     17%  small run

other tests (1)

test          result  vs the others in its run   difficulty      counts  why
GPQA Diamond   33.9%  ▁▁▁▁●▄▆▄▅▇█▅ beats 25/211  hard · avg 39%     35%  sharp

## 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   62% ▇▇▇▇▇▇▇
Epoch AI                                        1   35% ▇▇▇▇
prior: pull toward the middle                        2% ▇
weighted average                                   = 45.4

_{Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))} ## 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 Eurus-2-7B-PRIME (Q4_K_M, needs ~4.7 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Eurus-2-7B-PRIME-GGUF/tree/82a7007d3fe800366e5eeaf7602515a6021fcd62)) ```sh llama cli -hf bartowski/Eurus-2-7B-PRIME-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf bartowski/Eurus-2-7B-PRIME-GGUF:Q4_K_M ``` Other versions: Q8_0 (~8.1 GB) `llama cli -hf bartowski/Eurus-2-7B-PRIME-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-PRIME (Q4_K_M, needs ~4.7 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Eurus-2-7B-PRIME-GGUF/tree/82a7007d3fe800366e5eeaf7602515a6021fcd62)) ```sh lms get https://huggingface.co/bartowski/Eurus-2-7B-PRIME-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/bartowski/Eurus-2-7B-PRIME-GGUF@Q8_0 -y` · 8bit (~8.1 GB, Mac only) `lms get https://huggingface.co/WaveCut/PRIME-RL_Eurus-2-7B-PRIME-Q8-mlx -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-PRIME (Q4_K_M, needs ~4.7 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Eurus-2-7B-PRIME-GGUF/tree/82a7007d3fe800366e5eeaf7602515a6021fcd62)) ```sh ollama run hf.co/bartowski/Eurus-2-7B-PRIME-GGUF:Q4_K_M ``` Other versions: Q8_0 (~8.1 GB) `ollama run hf.co/bartowski/Eurus-2-7B-PRIME-GGUF: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 Eurus-2-7B-PRIME (8bit, needs ~8.1 GB, Mac only, from [huggingface.co/WaveCut](https://huggingface.co/WaveCut/PRIME-RL_Eurus-2-7B-PRIME-Q8-mlx/tree/58bb62b06dae2a628b4ab31ca4f3f49ae954eba8)) ```sh mlx_lm.chat --model WaveCut/PRIME-RL_Eurus-2-7B-PRIME-Q8-mlx ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model WaveCut/PRIME-RL_Eurus-2-7B-PRIME-Q8-mlx ``` ### Other ways to run it 1. Run it as published (fp32, needs ~37.08 GB, NVIDIA only, from [huggingface.co/PRIME-RL](https://huggingface.co/PRIME-RL/Eurus-2-7B-PRIME)) ```sh vllm serve PRIME-RL/Eurus-2-7B-PRIME ``` Model repository and documentation: [huggingface.co/PRIME-RL](https://huggingface.co/PRIME-RL/Eurus-2-7B-PRIME) [interactive version](https://whatmodel.sh/model/eurus-2-7b-prime) · [JSON](https://whatmodel.sh/api?format=json&all=1)