aniris/gemma-2-2b-it-Q4_K_M-GGUF
This model was converted to GGUF format from unsloth/gemma-2-2b-it using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
llama.cpp์ฉ GGUF(Q4_K_M) ํ์์ผ๋ก ๋ณํํ ๋ชจ๋ธ์ ๋๋ค.
๊ฐ๋ฒผ์ด ๋ก์ปฌ ์ฑ๋ด, ์ฝ๋ ๋ณด์กฐ, ๊ฐ๋จํ ์คํ์ฉ์ผ๋ก ์ ํฉํฉ๋๋ค.
๊ณ ์ ๋ฐ ์ถ๋ก , ์ต์ ์ ๋ณด, ๊ฐํ ์ฌ์ค์ฑ์ด ๊ผญ ํ์ํ ์ฉ๋๋ผ๋ฉด ๋ ํฐ ๋ชจ๋ธ์ด๋ ๊ณ ์ ๋ฐ ๋ฒ์ ์ ํจ๊ป ๊ฒํ ํ๋ ๊ฒ์ ๊ถ์ฅํฉ๋๋ค.
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 aniris/gemma-2-2b-it-Q4_K_M-GGUF --hf-file gemma-2-2b-it-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo aniris/gemma-2-2b-it-Q4_K_M-GGUF --hf-file gemma-2-2b-it-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 aniris/gemma-2-2b-it-Q4_K_M-GGUF --hf-file gemma-2-2b-it-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo aniris/gemma-2-2b-it-Q4_K_M-GGUF --hf-file gemma-2-2b-it-q4_k_m.gguf -c 2048
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Base model
unsloth/gemma-2-2b-it