[whatmodel.sh](../../README.md) / EXAONE 3.5 2.4B Instruct
# EXAONE 3.5 2.4B Instruct
LG AI Research · released 2024-12 · 2.41B params · exaone
**score 37.5** ±2.1 · #602 of 732 · from 7 benchmark results across 3 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks EXAONE 3.5 2.4B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #405 of 469 21% 4
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #279 of 402* 39% 2
coding · #405 of 469 · 21% of its score
Beats [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Qwen3.5 2B](qwen3-5-2b.md), [DeepSeek R1 Distill Llama 8B](deepseek-r1-distill-llama-8b.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
CodeReviewQA: solution identifica… 26.7% ▃··▁▂▂●▂█▃▄▂ beats 17/47 medium · avg 51% 7.8% sharp · noisy
CodeReviewQA: automated code refi… 3.8% ▁●▁▁▁▁▂▂▃█▅▄ beats 2/47 hard · avg 32% 7.5% typical
CodeReviewQA: change localisation… 0.8% ●▂▁▁▂▂▂▃▅▅▅▇ beats 8/47 hard · avg 33% 5.0% sharp · too hard for it · noisy
CodeReviewQA: change type recogni… 65.3% ▁▁▁······▁●█ beats 13/47 easy · avg 67% 0.5% blunt · at its level · noisy
chat & writing · #279 of 402 · 39% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Ministral 3 14B](ministral-3-14b.md), [Ministral 3 8B](ministral-3-8b.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
Arena-Hard (version/judge unspeci… 48.2% ▃▃▂▅●▆▅█▅▅▂▃ beats 8/30 easy · avg 68% 21% at its level · small run
IFEval (aggregation unspecified) … 73.6% ▄·●·▄·▄▄···█ beats 1/6 easy · avg 78% 18% at its level
other tests (1)
test result vs the others in its run difficulty counts why
CPTUB average (SpeakLeash, 0-5 ju… 33.4% ▁●▂▂▃█▃▆▅▄▂▃ beats 3/95 medium · avg 65% 39% at its level
source results share of the score
SpeakLeash CPTUB 1 39% ▇▇▇▇▇
LG AI Research vendor-reported benchmar… 2 39% ▇▇▇▇▇
CodeReviewQA: The Code Review Comprehen… 4 21% ▇▇
prior: pull toward the middle 2% ▇
weighted average = 37.5
Licences: SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([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 EXAONE 3.5 2.4B Instruct (Q4_K_M, needs ~1.6 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/EXAONE-3.5-2.4B-Instruct-GGUF/tree/347ad15365d7e4c4fb0d206e07b87bdefe9e67d9))
```sh
llama cli -hf lmstudio-community/EXAONE-3.5-2.4B-Instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/EXAONE-3.5-2.4B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~2.8 GB) `llama cli -hf lmstudio-community/EXAONE-3.5-2.4B-Instruct-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 EXAONE 3.5 2.4B Instruct (Q4_K_M, needs ~1.6 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/EXAONE-3.5-2.4B-Instruct-GGUF/tree/347ad15365d7e4c4fb0d206e07b87bdefe9e67d9))
```sh
lms get https://huggingface.co/lmstudio-community/EXAONE-3.5-2.4B-Instruct-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 (~2.8 GB) `lms get https://huggingface.co/lmstudio-community/EXAONE-3.5-2.4B-Instruct-GGUF@Q8_0 -y` · 4bit (~1.4 GB, Mac only) `lms get https://huggingface.co/alexgusevski/EXAONE-3.5-2.4B-Instruct-q4-mlx -y` · 8bit (~2.6 GB, Mac only) `lms get https://huggingface.co/alexgusevski/EXAONE-3.5-2.4B-Instruct-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 EXAONE 3.5 2.4B Instruct (Q4_K_M, needs ~1.95 GB, from [ollama.com](https://ollama.com/library/exaone3.5/tags))
```sh
ollama run exaone3.5:2.4b-instruct-q4_K_M
```
Other versions: Q8_0 (~3.39 GB) `ollama run exaone3.5:2.4b-instruct-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 EXAONE 3.5 2.4B Instruct (4bit, needs ~1.4 GB, Mac only, from [huggingface.co/alexgusevski](https://huggingface.co/alexgusevski/EXAONE-3.5-2.4B-Instruct-q4-mlx/tree/e1908c1785ab5242c933ff5472c1bda9b8cfd724))
```sh
mlx_lm.chat --model alexgusevski/EXAONE-3.5-2.4B-Instruct-q4-mlx
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model alexgusevski/EXAONE-3.5-2.4B-Instruct-q4-mlx
```
Other versions: 8bit (~2.6 GB, Mac only) `mlx_lm.chat --model alexgusevski/EXAONE-3.5-2.4B-Instruct-q8-mlx`
Model repository and documentation: [huggingface.co/LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct)
[interactive version](https://whatmodel.sh/model/exaone-3-5-2-4b) · [JSON](https://whatmodel.sh/api?format=json&all=1)