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.
- Libraries
- llama-cpp-python
How to use DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix 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-imatrix", filename="Psyonic-Cetacean-Ultra-Quality-20B-IQ3_XS-imat.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-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 Studio
How to use DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix 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-imatrix 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-imatrix 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-imatrix to start chatting
- Atomic Chat new
- 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
OpenVINO IR format with Optimum
Hello!
This work is awesome! Can these models be converted to the OpenVINO IR format? Model cards mention custom quantizations methods that are not discussed in the intel documentation. I am running Arc Gpus with Vulkan drivers for GGUF but need to leverage the intel stack ai dev tools for faster inference. Intel documentation doesnt discuss applying the IR format to models that have already been quantized, only from full precision, which may challenge my abiity to leverage your models since they are in GGUF.
Let me know what you think, and this is awesome work.
Thank you for the compliments.;
RE: OpenVINO IR - can you reply with a link?
RE: Full precision.
To create the full version you need Mergekit and the mergefile + source files.
This will then create a source version you could create and translate into any "quant" or format.
The mergekit file / formula is located here:
https://huggingface.co/jebcarter/psyonic-cetacean-20B
No problem.
Check these out for an overview;
https://docs.openvino.ai/2024/openvino-workflow/model-preparation/convert-model-to-ir.html
https://docs.openvino.ai/2024/openvino-workflow/model-preparation/conversion-parameters.html
TLDR; intel is propping up their gpu ecosystem on a model serving framework meant for production use cases. The goal appears to be drop-in substitutes for popular libraries to enrich the value proposition of switching to intel hardware- hop over to team blue and leave some tech debt at the door. If you use pandas, "Modin" data frames are supposed to be faster on intel hardware without requiring major rewrites.
On CPU only I saw HUGE speedups in my testing with cognitivecomputations/dolphin-2.9.4-llama3.1-8b-fp16 yesterday for a few prompts. Working on synthetic data generation so speedy inference is huge.