This model was converted to GGUF format from LGAI-EXAONE/EXAONE-4.0-1.2B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

benchmark 1.2B Reasoning Mode

	EXAONE 4.0 1.2B	EXAONE Deep 2.4B	Qwen 3 0.6B	Qwen 3 1.7B	SmolLM 3 3B
Model Size	1.28B	2.41B	596M	1.72B	3.08B
Hybrid Reasoning	✅		✅	✅	✅
World Knowledge
MMLU-Redux	71.5	68.9	55.6	73.9	74.8
MMLU-Pro	59.3	56.4	38.3	57.7	57.8
GPQA-Diamond	52.0	54.3	27.9	40.1	41.7
Math/Coding
AIME 2025	45.2	47.9	15.1	36.8	36.7
HMMT Feb 2025	34.0	27.3	7.0	21.8	26.0
LiveCodeBench v5	44.6	47.2	12.3	33.2	27.6
LiveCodeBench v6	45.3	43.1	16.4	29.9	29.1
Instruction Following
IFEval	67.8	71.0	59.2	72.5	71.2
Multi-IF (EN)	53.9	54.5	37.5	53.5	47.5
Agentic Tool Use
BFCL-v3	52.9	N/A	46.4	56.6	37.1
Tau-Bench (Airline)	20.5	N/A	22.0	31.0	37.0
Tau-Bench (Retail)	28.1	N/A	3.3	6.5	5.4
Multilinguality
KMMLU-Pro	42.7	24.6	21.6	38.3	30.5
KMMLU-Redux	46.9	25.0	24.5	38.0	33.7
KSM	60.6	60.9	22.8	52.9	49.7
MMMLU (ES)	62.4	51.4	48.8	64.5	64.7
MATH500 (ES)	88.8	84.5	70.6	87.9	87.5

1.2B Non-Reasoning Mode

	EXAONE 4.0 1.2B	Qwen 3 0.6B	Gemma 3 1B	Qwen 3 1.7B	SmolLM 3 3B
Model Size	1.28B	596M	1.00B	1.72B	3.08B
Hybrid Reasoning	✅	✅		✅	✅
World Knowledge
MMLU-Redux	66.9	44.6	40.9	63.4	65.0
MMLU-Pro	52.0	26.6	14.7	43.7	43.6
GPQA-Diamond	40.1	22.9	19.2	28.6	35.7
Math/Coding
AIME 2025	23.5	2.6	2.1	9.8	9.3
HMMT Feb 2025	13.0	1.0	1.5	5.1	4.7
LiveCodeBench v5	26.4	3.6	1.8	11.6	11.4
LiveCodeBench v6	30.1	6.9	2.3	16.6	20.6
Instruction Following
IFEval	74.7	54.5	80.2	68.2	76.7
Multi-IF (EN)	62.1	37.5	32.5	51.0	51.9
Long Context
HELMET	41.2	21.1	N/A	33.8	38.6
RULER	77.4	55.1	N/A	65.9	66.3
LongBench v1	36.9	32.4	N/A	41.9	39.9
Agentic Tool Use
BFCL-v3	55.7	44.1	N/A	52.2	47.3
Tau-Bench (Airline)	10.0	31.5	N/A	13.5	38.0
Tau-Bench (Retail)	21.7	5.7	N/A	4.6	6.7
Multilinguality
KMMLU-Pro	37.5	24.6	9.7	29.5	27.6
KMMLU-Redux	40.4	22.8	19.4	29.8	26.4
KSM	26.3	0.1	22.8	16.3	16.1
Ko-LongBench	69.8	16.4	N/A	57.1	15.7
MMMLU (ES)	54.6	39.5	35.9	54.3	55.1
MATH500 (ES)	71.2	38.5	41.2	66.0	62.4
WMT24++ (ES)	65.9	58.2	76.9	76.7	84.0

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

bash

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Edge-Quant/EXAONE-4.0-1.2B-Q4_K_M-GGUF --hf-file exaone-4.0-1.2b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Edge-Quant/EXAONE-4.0-1.2B-Q4_K_M-GGUF --hf-file exaone-4.0-1.2b-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Edge-Quant/EXAONE-4.0-1.2B-Q4_K_M-GGUF --hf-file exaone-4.0-1.2b-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Edge-Quant/EXAONE-4.0-1.2B-Q4_K_M-GGUF --hf-file exaone-4.0-1.2b-q4_k_m.gguf -c 2048