---
license: other
license_name: exaone
license_link: LICENSE
language:
- en
- ko
tags:
- lg-ai
- exaone
- exaone-3.5
- TensorBlock
- GGUF
pipeline_tag: text-generation
library_name: transformers
base_model: LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct
---

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        <p style="margin-top: 0.5em; margin-bottom: 0em;">
            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
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## LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct - GGUF

This repo contains GGUF format model files for [LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template

```
[|system|]{system_prompt}[|endofturn|]
[|user|]{prompt}
[|assistant|]
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [EXAONE-3.5-2.4B-Instruct-Q2_K.gguf](https://huggingface.co/tensorblock/EXAONE-3.5-2.4B-Instruct-GGUF/blob/main/EXAONE-3.5-2.4B-Instruct-Q2_K.gguf) | Q2_K | 1.010 GB | smallest, significant quality loss - not recommended for most purposes |
| [EXAONE-3.5-2.4B-Instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/EXAONE-3.5-2.4B-Instruct-GGUF/blob/main/EXAONE-3.5-2.4B-Instruct-Q3_K_S.gguf) | Q3_K_S | 1.141 GB | very small, high quality loss |
| [EXAONE-3.5-2.4B-Instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/EXAONE-3.5-2.4B-Instruct-GGUF/blob/main/EXAONE-3.5-2.4B-Instruct-Q3_K_M.gguf) | Q3_K_M | 1.249 GB | very small, high quality loss |
| [EXAONE-3.5-2.4B-Instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/EXAONE-3.5-2.4B-Instruct-GGUF/blob/main/EXAONE-3.5-2.4B-Instruct-Q3_K_L.gguf) | Q3_K_L | 1.346 GB | small, substantial quality loss |
| [EXAONE-3.5-2.4B-Instruct-Q4_0.gguf](https://huggingface.co/tensorblock/EXAONE-3.5-2.4B-Instruct-GGUF/blob/main/EXAONE-3.5-2.4B-Instruct-Q4_0.gguf) | Q4_0 | 1.425 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [EXAONE-3.5-2.4B-Instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/EXAONE-3.5-2.4B-Instruct-GGUF/blob/main/EXAONE-3.5-2.4B-Instruct-Q4_K_S.gguf) | Q4_K_S | 1.433 GB | small, greater quality loss |
| [EXAONE-3.5-2.4B-Instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/EXAONE-3.5-2.4B-Instruct-GGUF/blob/main/EXAONE-3.5-2.4B-Instruct-Q4_K_M.gguf) | Q4_K_M | 1.497 GB | medium, balanced quality - recommended |
| [EXAONE-3.5-2.4B-Instruct-Q5_0.gguf](https://huggingface.co/tensorblock/EXAONE-3.5-2.4B-Instruct-GGUF/blob/main/EXAONE-3.5-2.4B-Instruct-Q5_0.gguf) | Q5_0 | 1.693 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [EXAONE-3.5-2.4B-Instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/EXAONE-3.5-2.4B-Instruct-GGUF/blob/main/EXAONE-3.5-2.4B-Instruct-Q5_K_S.gguf) | Q5_K_S | 1.693 GB | large, low quality loss - recommended |
| [EXAONE-3.5-2.4B-Instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/EXAONE-3.5-2.4B-Instruct-GGUF/blob/main/EXAONE-3.5-2.4B-Instruct-Q5_K_M.gguf) | Q5_K_M | 1.730 GB | large, very low quality loss - recommended |
| [EXAONE-3.5-2.4B-Instruct-Q6_K.gguf](https://huggingface.co/tensorblock/EXAONE-3.5-2.4B-Instruct-GGUF/blob/main/EXAONE-3.5-2.4B-Instruct-Q6_K.gguf) | Q6_K | 1.978 GB | very large, extremely low quality loss |
| [EXAONE-3.5-2.4B-Instruct-Q8_0.gguf](https://huggingface.co/tensorblock/EXAONE-3.5-2.4B-Instruct-GGUF/blob/main/EXAONE-3.5-2.4B-Instruct-Q8_0.gguf) | Q8_0 | 2.560 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/EXAONE-3.5-2.4B-Instruct-GGUF --include "EXAONE-3.5-2.4B-Instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/EXAONE-3.5-2.4B-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```