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balrogbob/open_llama_3b_v2-python-instruct-0.1-Q2_K-GGUF
open_llama_3b_v2-python-instruct-0.1-Q2_K-GGUF is a machine learning model from balrogbob. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
This model was converted to GGUF format from balrogbob/openllama3bv2-python-instruct-0.1 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 balrogbob/open_llama_3b_v2-python-instruct-0.1 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 balrogbob/open_llama_3b_v2-python-instruct-0.1-Q2_K-GGUF --hf-file open_llama_3b_v2-python-instruct-0.1-q2_k.gguf -p "The meaning to life and the universe is"
llama-server --hf-repo balrogbob/open_llama_3b_v2-python-instruct-0.1-Q2_K-GGUF --hf-file open_llama_3b_v2-python-instruct-0.1-q2_k.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 balrogbob/open_llama_3b_v2-python-instruct-0.1-Q2_K-GGUF --hf-file open_llama_3b_v2-python-instruct-0.1-q2_k.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo balrogbob/open_llama_3b_v2-python-instruct-0.1-Q2_K-GGUF --hf-file open_llama_3b_v2-python-instruct-0.1-q2_k.gguf -c 2048