tensorblock/EXAONE-3.5-2.4B-Instruct-GGUF

Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server

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.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

Run them on the TensorBlock client using your local machine ↗

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	Q2_K	1.010 GB	smallest, significant quality loss - not recommended for most purposes
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	Q3_K_M	1.249 GB	very small, high quality loss
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	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	Q4_K_S	1.433 GB	small, greater quality loss
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	Q5_0	1.693 GB	legacy; medium, balanced quality - prefer using Q4_K_M
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	Q5_K_M	1.730 GB	large, very low quality loss - recommended
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	Q8_0	2.560 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

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:

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'