Instructions to use DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF", filename="Psyonic-Cetacean-Ultra-IQ4_NL.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 DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-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 DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF:Q4_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 DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF:Q4_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 DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF:Q4_K_M
- Ollama
How to use DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF with Ollama:
ollama run hf.co/DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF:Q4_K_M
- Unsloth Studio
How to use DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-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 DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-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 DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF with Docker Model Runner:
docker model run hf.co/DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF:Q4_K_M
- Lemonade
How to use DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Psyonic-Cetacean-Ultra-Quality-20b-GGUF-Q4_K_M
List all available models
lemonade list
Context size 4096 tokens?
Does anyone have tested this quant with higher context size? Does this work fine with YaRN? Which context sizes are achievable?
With Llama2s you can use rope ; see the very bottom of this page for rope info:
https://huggingface.co/DavidAU/TieFighter-Holodeck-Holomax-Mythomax-F1-V1-COMPOS-20B-gguf
NOTE:
This might be outdated some what, as this is 6 months old.