Abhi99999/open0-2-lite-Q4_K_M-GGUF

This model was converted to GGUF format from aquigpt/open0-2-lite using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Performance Benchmarks

Aqui-open0-2 Lite demonstrates exceptional performance across multiple challenging benchmarks, significantly outperforming other models in its size class:

Benchmark	Aqui-open0-2 Lite (1.72B)	Gemma 3 (1B)	Qwen3 (2.03B)	Llama 3.2 (1.24B)	LFM2 (1.17B)
MMLU (General Knowledge)	67.5%	40.1%	59.1%	46.6%	55.2%
GPQA (Science)	31.8%	19.2%	27.7%	19.6%	31.5%
IFEval (Instruction Following)	73.4%	62.9%	68.4%	52.4%	74.5%
GSM8K (Grade School Math)	63.2%	59.6%	51.4%	35.7%	58.3%
MGSM (Multilingual)	70.2%	43.6%	66.6%	29.1%	55.0%
Average Performance	61.2%	45.1%	54.6%	36.7%	54.9%

Bold: Best performance, Italics: Second best

Use with llama.cpp

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

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Abhi99999/open0-2-lite-Q4_K_M-GGUF --hf-file open0-2-lite-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Abhi99999/open0-2-lite-Q4_K_M-GGUF --hf-file open0-2-lite-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 Abhi99999/open0-2-lite-Q4_K_M-GGUF --hf-file open0-2-lite-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Abhi99999/open0-2-lite-Q4_K_M-GGUF --hf-file open0-2-lite-q4_k_m.gguf -c 2048