[whatmodel.sh](../../README.md) / EXAONE Deep 7.8B # EXAONE Deep 7.8B LG AI Research · released 2025-03 · 7.82B params · exaone **score 53.8** ±1.5 · #307 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 7.8B among every model on that task or test, worst to best.}

task    vs the others         rank  counts  tests
coding  █●·········█      #2 of 3*    9.3%      1
math    ▁▁▃▅█●▄█▄▂▁▁   #221 of 431     57%      3

coding · #2 of 3 · 9.3% of its score

test                                result  vs the others in its run  difficulty        counts  why
LiveCodeBench (version unspecifie…   55.2%  ▁···▁▁▁█●▃▄▁ beats 21/38  medium · avg 45%    9.3%  at its level · small run

math · #221 of 431 · 57% of its score

Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md) here, though they rank higher overall. Trails [Phi-2 (2.7B)](phi-2.md), [Granite 8B Code Base (4K)](granite-8b-code-base-4k.md), [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md) here, though they rank lower overall.

test               result  vs the others in its run  difficulty           counts  why
AIME 2025 (maker)   59.6%  █·●▄····▄··█ beats 2/6    medium · avg 54%        29%  sharp · at its level
AIME 2024 (maker)   70.0%  ▁▁▁▂▁▃▁▅●██▃ beats 36/85  medium · avg 56%        19%  sharp · at its level · small run
MATH-500 (maker)    94.8%  ▁▃▂▃▄▇▅●█▄▂▁ beats 53/85  very easy · avg 92%     10%  maxed out · small run

other tests (1)

test                  result  vs the others in its run  difficulty      counts  why
GPQA Diamond (maker)   62.6%  █···●▄···▄·█ beats 2/6    hard · avg 51%     33%  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                                   = 53.8

## 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 7.8B (Q4_K_M, needs ~4.77 GB, from [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-7.8B-GGUF/tree/b317545742931021fc291324c30cefb649e3dcad)) ```sh llama cli -hf LGAI-EXAONE/EXAONE-Deep-7.8B-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-7.8B-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 7.8B (Q4_K_M, needs ~4.77 GB, from [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-7.8B-GGUF/tree/b317545742931021fc291324c30cefb649e3dcad)) ```sh lms get https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-7.8B-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: 8bit (~8.3 GB, Mac only) `lms get https://huggingface.co/sinbal/EXAONE-Deep-7.8B-MLX-8bit -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 EXAONE Deep 7.8B (Q4_K_M, needs ~4.8 GB, from [ollama.com](https://ollama.com/library/exaone-deep/tags)) ```sh ollama run exaone-deep:7.8b ``` ### 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 EXAONE Deep 7.8B (8bit, needs ~8.3 GB, Mac only, from [huggingface.co/sinbal](https://huggingface.co/sinbal/EXAONE-Deep-7.8B-MLX-8bit/tree/931074a8ba025dc4cab44076c76c6b9f74c2feee)) ```sh mlx_lm.chat --model sinbal/EXAONE-Deep-7.8B-MLX-8bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model sinbal/EXAONE-Deep-7.8B-MLX-8bit ``` Model repository and documentation: [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-7.8B) [interactive version](https://whatmodel.sh/model/exaone-deep-7-8b) · [JSON](https://whatmodel.sh/api?format=json&all=1)