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redhat6/Llama-3.1-Korean-8B-Instruct-Q4_K_M-GGUF
Llama-3.1-Korean-8B-Instruct-Q4_K_M-GGUF is a machine learning model from redhat6. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This model was converted to GGUF format from sh2orc/Llama-3.1-Korean-8B-Instruct using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.
Downloads · 30 days
57
17% of all-time downloads
All-time downloads
330
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From the Hugging Face model README
This model was converted to GGUF format from sh2orc/Llama-3.1-Korean-8B-Instruct using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Create the model in Ollama
ollama create Llama-3.1-Korean-8B-Instruct-Q4_K_M -f Modelfile
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
llama-cli --hf-repo redhat6/Llama-3.1-Korean-8B-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-korean-8b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
llama-server --hf-repo redhat6/Llama-3.1-Korean-8B-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-korean-8b-instruct-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 redhat6/Llama-3.1-Korean-8B-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-korean-8b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo redhat6/Llama-3.1-Korean-8B-Instruct-Q4_K_M-GGUF --hf-file llama-3.1-korean-8b-instruct-q4_k_m.gguf -c 2048