Instructions to use DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix 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-imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix: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-imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix: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-imatrix:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix: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-imatrix:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix:Q4_K_M

Use Docker

docker model run hf.co/DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix:Q4_K_M

LM Studio
Jan

vLLM

How to use DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix 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-imatrix"
# 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-imatrix",
		"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-imatrix:Q4_K_M

Ollama
How to use DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix with Ollama:
```
ollama run hf.co/DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix with Docker Model Runner:
```
docker model run hf.co/DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix:Q4_K_M
```

Lemonade

How to use DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix:Q4_K_M

Run and chat with the model

lemonade run user.Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix-Q4_K_M

List all available models

lemonade list

Atomic Chat

Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix / Psyonic-Cetacean-Ultra-Quality-imat-Q5_K_S-imat.gguf

DavidAU

Upload Psyonic-Cetacean-Ultra-Quality-imat-Q5_K_S-imat.gguf

cf6d0d4 verified over 2 years ago

History Blame GGUF Editor new Delete

13.8 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 8a030d7f7d9241f5afcb73b9eea1eb130b6f0cd30152d704822ff63d946f6e5c
Size of remote file:: 13.8 GB
SHA256:: 1cbd28712d0d1f24ad339ff1c2e990d3455551187569e55b7f4df586fac40953

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.