Instructions to use FPHam/Regency_Bewildered_12B_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use FPHam/Regency_Bewildered_12B_GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="FPHam/Regency_Bewildered_12B_GGUF", filename="Regency_Bewildered_12B_Q4_K_M_o.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use FPHam/Regency_Bewildered_12B_GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf FPHam/Regency_Bewildered_12B_GGUF:Q4_K_M_O # Run inference directly in the terminal: llama-cli -hf FPHam/Regency_Bewildered_12B_GGUF:Q4_K_M_O
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf FPHam/Regency_Bewildered_12B_GGUF:Q4_K_M_O # Run inference directly in the terminal: llama-cli -hf FPHam/Regency_Bewildered_12B_GGUF:Q4_K_M_O
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 FPHam/Regency_Bewildered_12B_GGUF:Q4_K_M_O # Run inference directly in the terminal: ./llama-cli -hf FPHam/Regency_Bewildered_12B_GGUF:Q4_K_M_O
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 FPHam/Regency_Bewildered_12B_GGUF:Q4_K_M_O # Run inference directly in the terminal: ./build/bin/llama-cli -hf FPHam/Regency_Bewildered_12B_GGUF:Q4_K_M_O
Use Docker
docker model run hf.co/FPHam/Regency_Bewildered_12B_GGUF:Q4_K_M_O
- LM Studio
- Jan
- Ollama
How to use FPHam/Regency_Bewildered_12B_GGUF with Ollama:
ollama run hf.co/FPHam/Regency_Bewildered_12B_GGUF:Q4_K_M_O
- Unsloth Studio
How to use FPHam/Regency_Bewildered_12B_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 FPHam/Regency_Bewildered_12B_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 FPHam/Regency_Bewildered_12B_GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for FPHam/Regency_Bewildered_12B_GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use FPHam/Regency_Bewildered_12B_GGUF with Docker Model Runner:
docker model run hf.co/FPHam/Regency_Bewildered_12B_GGUF:Q4_K_M_O
- Lemonade
How to use FPHam/Regency_Bewildered_12B_GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull FPHam/Regency_Bewildered_12B_GGUF:Q4_K_M_O
Run and chat with the model
lemonade run user.Regency_Bewildered_12B_GGUF-Q4_K_M_O
List all available models
lemonade list
Funny model!! Need a smaller quant if possible.
#3 opened 11 months ago
by
Kurgan1138
Glazing-Sycophancy Testing
#2 opened 11 months ago
by
SandInTheDunes
Reply is generated twice
2
#1 opened 11 months ago
by
SandInTheDunes