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 (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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 serve -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
Reply is generated twice
For some reason the model reply is generated once, then underneath the same reply would be generated once more in the /* */ section. I see that this model uses a manual prompt template (Alpaca) instead of Jinja (in LM Studio).
Interesting, some other chats instances I tried did not have this issue.
It should not use alpaca, but the hf to gghuf I used didn't apparenlly added the jinja correctly, at least nor for LM studio so this falls back to alpaca. I have to revisit this again, don't know where are all the bits and bobs from this model on my disk.