F-urkan/rStar2-Agent-14B-Q4_0-GGUF
228
rStar2-Agent-14B-Q4_0-GGUF
This model was converted to GGUF format from `rstar2-reproduce/rStar2-Agent-14B` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.
Use with Llamafile :)
Portable and fast...
wget "https://github.com/Mozilla-Ocho/llamafile/releases/download/0.9.3/llamafile-0.9.3"
chmod +x llamafile-0.9.3
wget "https://huggingface.co/F-urkan/rStar2-Agent-14B-Q4_0-GGUF/resolve/main/rstar2-agent-14b-q4_0.gguf"
./llamafile-0.9.3 --server --port 8081 --no-mmap --nobrowser -ngl 9999 -m rstar2-agent-14b-q4_0.ggufRequirements:
- 8 gb vram
- 8 gb disk space
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 F-urkan/rStar2-Agent-14B-Q4_0-GGUF --hf-file rstar2-agent-14b-q4_0.gguf -p "The meaning to life and the universe is"Server:
llama-server --hf-repo F-urkan/rStar2-Agent-14B-Q4_0-GGUF --hf-file rstar2-agent-14b-q4_0.gguf -c 2048Note: 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.cppStep 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 makeStep 3: Run inference through the main binary.
./llama-cli --hf-repo F-urkan/rStar2-Agent-14B-Q4_0-GGUF --hf-file rstar2-agent-14b-q4_0.gguf -p "The meaning to life and the universe is"or
./llama-server --hf-repo F-urkan/rStar2-Agent-14B-Q4_0-GGUF --hf-file rstar2-agent-14b-q4_0.gguf -c 2048License
MIT
Citation
@misc{shang2025rstar2agent,
title={rStar2-Agent: Agentic Reasoning Technical Report},
author={Ning Shang and Yifei Liu and Yi Zhu and Li Lyna Zhang and Weijiang Xu and Xinyu Guan and Buze Zhang and Bingcheng Dong and Xudong Zhou and Bowen Zhang and Ying Xin and Ziming Miao and Scarlett Li and Fan Yang and Mao Yang},
year={2025},
eprint={2508.20722},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2508.20722},
}