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goodasdgood/WizardCoder-Python-34B-V1.0-Q2_K-GGUF
WizardCoder-Python-34B-V1.0-Q2_K-GGUF is a machine learning model from goodasdgood. 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 llama2.
This model was converted to GGUF format from WizardLMTeam/WizardCoder-Python-34B-V1.0 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
16
6% of all-time downloads
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.gguf12.5 GB · 100%
From the Hugging Face model README
This model was converted to GGUF format from WizardLMTeam/WizardCoder-Python-34B-V1.0 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 goodasdgood/WizardCoder-Python-34B-V1.0-Q2_K-GGUF --hf-file wizardcoder-python-34b-v1.0-q2_k.gguf -p "The meaning to life and the universe is"
llama-server --hf-repo goodasdgood/WizardCoder-Python-34B-V1.0-Q2_K-GGUF --hf-file wizardcoder-python-34b-v1.0-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 goodasdgood/WizardCoder-Python-34B-V1.0-Q2_K-GGUF --hf-file wizardcoder-python-34b-v1.0-q2_k.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo goodasdgood/WizardCoder-Python-34B-V1.0-Q2_K-GGUF --hf-file wizardcoder-python-34b-v1.0-q2_k.gguf -c 2048