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onlykit-labs/Meta-Llama-3.1-8B-Instruct-Summarizer-Q4_K_M-GGUF
Meta-Llama-3.1-8B-Instruct-Summarizer-Q4_K_M-GGUF is a summarization model from onlykit-labs. Use it when you need a shorter version of a longer text. The card lists the license as apache-2.0.
This model was converted to GGUF format from raaec/Meta-Llama-3.1-8B-Instruct-Summarizer 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
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From the Hugging Face model README
This model was converted to GGUF format from raaec/Meta-Llama-3.1-8B-Instruct-Summarizer using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
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 onlykit-labs/Meta-Llama-3.1-8B-Instruct-Summarizer-Q4_K_M-GGUF --hf-file meta-llama-3.1-8b-instruct-summarizer-q4_k_m.gguf -p "The meaning to life and the universe is"
llama-server --hf-repo onlykit-labs/Meta-Llama-3.1-8B-Instruct-Summarizer-Q4_K_M-GGUF --hf-file meta-llama-3.1-8b-instruct-summarizer-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 onlykit-labs/Meta-Llama-3.1-8B-Instruct-Summarizer-Q4_K_M-GGUF --hf-file meta-llama-3.1-8b-instruct-summarizer-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo onlykit-labs/Meta-Llama-3.1-8B-Instruct-Summarizer-Q4_K_M-GGUF --hf-file meta-llama-3.1-8b-instruct-summarizer-q4_k_m.gguf -c 2048