language:
- en
license: mit
tags: - llama-cpp
- gguf-my-repo
base_model: RESMPDEV/Wukong-Phi-3-Instruct-Ablated
datasets: - cognitivecomputations/Dolphin-2.9
uncensored: - yes
v8karlo/Wukong-Phi-3-Instruct-Ablated-Q4_K_M-GGUF
UNCENSORED Phi-3 model.
This model was converted to GGUF format from RESMPDEV/Wukong-Phi-3-Instruct-Ablated using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Convert Safetensors to GGUF . https://huggingface.co/spaces/ggml-org/gguf-my-repo .
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 --hf-repo v8karlo/Wukong-Phi-3-Instruct-Ablated-Q4_K_M-GGUF --hf-file wukong-phi-3-instruct-ablated-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo v8karlo/Wukong-Phi-3-Instruct-Ablated-Q4_K_M-GGUF --hf-file wukong-phi-3-instruct-ablated-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.
./main --hf-repo v8karlo/Wukong-Phi-3-Instruct-Ablated-Q4_K_M-GGUF --hf-file wukong-phi-3-instruct-ablated-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./server --hf-repo v8karlo/Wukong-Phi-3-Instruct-Ablated-Q4_K_M-GGUF --hf-file wukong-phi-3-instruct-ablated-q4_k_m.gguf -c 2048