metadata
base_model: meta-llama/Meta-Llama-3-8B
language:
- en
license: llama3
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
extra_gated_prompt: >-
### META LLAMA 3 COMMUNITY LICENSE AGREEMENT
Meta Llama 3 Version Release Date: April 18, 2024
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### Meta Llama 3 Acceptable Use Policy
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[https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)
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Date of birth: date_picker
Country: country
Affiliation: text
geo: ip_location
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huggioface/Meta-Llama-3-8B-Q5_K_M-GGUF
This model was converted to GGUF format from meta-llama/Meta-Llama-3-8B 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 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 huggioface/Meta-Llama-3-8B-Q5_K_M-GGUF --hf-file meta-llama-3-8b-q5_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo huggioface/Meta-Llama-3-8B-Q5_K_M-GGUF --hf-file meta-llama-3-8b-q5_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 huggioface/Meta-Llama-3-8B-Q5_K_M-GGUF --hf-file meta-llama-3-8b-q5_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo huggioface/Meta-Llama-3-8B-Q5_K_M-GGUF --hf-file meta-llama-3-8b-q5_k_m.gguf -c 2048