[whatmodel.sh](../../README.md) / EXAONE Deep 2.4B # EXAONE Deep 2.4B LG AI Research · released 2025-03 · 2.41B params · exaone **score 49.8** ±1.5 · #374 of 732 · from 5 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 EXAONE Deep 2.4B among every model on that task or test, worst to best.}

task    vs the others         rank  counts  tests
coding  ●█·········█      #3 of 3*    9.0%      1
math    ▁▁▃▅█●▄█▄▂▁▁   #255 of 431     61%      3

coding · #3 of 3 · 9.0% of its score

test                                result  vs the others in its run  difficulty        counts  why
LiveCodeBench (version unspecifie…   46.6%  ▁···▁▁▁●▇▃▄▁ beats 14/38  medium · avg 45%    9.0%  at its level · small run

math · #255 of 431 · 61% of its score

Beats [Command A (03-2025)](command-a.md), [GPT-4o (2024-05-13)](gpt-4o-2024-05-13.md), [GPT-4o (2024-11-20)](gpt-4o-2024-11-20.md) here, though they rank higher overall. Trails [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Gemma 7B (base)](gemma-7b-base.md), [CodeGemma 7B (v1.0)](codegemma-7b.md) here, though they rank lower overall.

test               result  vs the others in its run  difficulty           counts  why
AIME 2025 (maker)   47.9%  ●·▄▄····▄··█ beats 1/6    medium · avg 54%        30%  sharp · at its level
AIME 2024 (maker)   52.5%  ▁▁▁▂▁▃▁●███▃ beats 24/85  medium · avg 56%        19%  sharp · at its level · small run
MATH-500 (maker)    92.3%  ▁▃▂▃▄▇●▇█▄▂▁ beats 43/85  very easy · avg 92%     11%  small run

other tests (1)

test                  result  vs the others in its run  difficulty      counts  why
GPQA Diamond (maker)   54.3%  ●···▄▄···▄·█ beats 1/6    hard · avg 51%     29%  sharp · at its level

## 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
LG AI Research vendor-reported benchmar…        5   99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 49.8

## 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 EXAONE Deep 2.4B (Q4_K_M, needs ~1.64 GB, from [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-2.4B-GGUF/tree/2deb04415c53af469e95704b76df5f9298f99083)) ```sh llama cli -hf LGAI-EXAONE/EXAONE-Deep-2.4B-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf LGAI-EXAONE/EXAONE-Deep-2.4B-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 EXAONE Deep 2.4B (Q4_K_M, needs ~1.64 GB, from [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-2.4B-GGUF/tree/2deb04415c53af469e95704b76df5f9298f99083)) ```sh lms get https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-2.4B-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run EXAONE Deep 2.4B (Q4_K_M, needs ~1.6 GB, from [ollama.com](https://ollama.com/library/exaone-deep/tags)) ```sh ollama run exaone-deep:2.4b ``` Model repository and documentation: [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-2.4B) [interactive version](https://whatmodel.sh/model/exaone-deep-2-4b) · [JSON](https://whatmodel.sh/api?format=json&all=1)