Instructions to use dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF:Q3_K_M # Run inference directly in the terminal: llama cli -hf dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF:Q3_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF:Q3_K_M # Run inference directly in the terminal: llama cli -hf dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF:Q3_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF:Q3_K_M # Run inference directly in the terminal: ./llama-cli -hf dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF:Q3_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF:Q3_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF:Q3_K_M
Use Docker
docker model run hf.co/dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF:Q3_K_M
- LM Studio
- Jan
- Ollama
How to use dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF with Ollama:
ollama run hf.co/dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF:Q3_K_M
- Unsloth Studio
How to use dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF to start chatting
- Docker Model Runner
How to use dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF with Docker Model Runner:
docker model run hf.co/dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF:Q3_K_M
- Lemonade
How to use dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF:Q3_K_M
Run and chat with the model
lemonade run user.Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF-Q3_K_M
List all available models
lemonade list
- Atomic Chat
dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF
This model was converted to GGUF format from cognitivecomputations/Wizard-Vicuna-30B-Uncensored 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 dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF --hf-file wizard-vicuna-30b-uncensored-q3_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF --hf-file wizard-vicuna-30b-uncensored-q3_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 dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF --hf-file wizard-vicuna-30b-uncensored-q3_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF --hf-file wizard-vicuna-30b-uncensored-q3_k_m.gguf -c 2048
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Model tree for dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF
Base model
QuixiAI/Wizard-Vicuna-30B-UncensoredDataset used to train dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard62.120
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard83.450
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard58.240
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard50.810
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard78.450
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard14.250
docker model run hf.co/dlwnsvlf/Wizard-Vicuna-30B-Uncensored-Q3_K_M-GGUF:Q3_K_M