Instructions to use CISCai/gorilla-openfunctions-v2-SOTA-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use CISCai/gorilla-openfunctions-v2-SOTA-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="CISCai/gorilla-openfunctions-v2-SOTA-GGUF", filename="gorilla-openfunctions-v2.IQ1_S.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use CISCai/gorilla-openfunctions-v2-SOTA-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S # Run inference directly in the terminal: llama-cli -hf CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S # Run inference directly in the terminal: llama-cli -hf CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S
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 CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S # Run inference directly in the terminal: ./llama-cli -hf CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S
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 CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S
Use Docker
docker model run hf.co/CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S
- LM Studio
- Jan
- vLLM
How to use CISCai/gorilla-openfunctions-v2-SOTA-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CISCai/gorilla-openfunctions-v2-SOTA-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CISCai/gorilla-openfunctions-v2-SOTA-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S
- Ollama
How to use CISCai/gorilla-openfunctions-v2-SOTA-GGUF with Ollama:
ollama run hf.co/CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S
- Unsloth Studio new
How to use CISCai/gorilla-openfunctions-v2-SOTA-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 CISCai/gorilla-openfunctions-v2-SOTA-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 CISCai/gorilla-openfunctions-v2-SOTA-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for CISCai/gorilla-openfunctions-v2-SOTA-GGUF to start chatting
- Pi new
How to use CISCai/gorilla-openfunctions-v2-SOTA-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use CISCai/gorilla-openfunctions-v2-SOTA-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S
Run Hermes
hermes
- Docker Model Runner
How to use CISCai/gorilla-openfunctions-v2-SOTA-GGUF with Docker Model Runner:
docker model run hf.co/CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S
- Lemonade
How to use CISCai/gorilla-openfunctions-v2-SOTA-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull CISCai/gorilla-openfunctions-v2-SOTA-GGUF:IQ1_S
Run and chat with the model
lemonade run user.gorilla-openfunctions-v2-SOTA-GGUF-IQ1_S
List all available models
lemonade list
Evaluation results?
Thanks a lot for quantizing with an imatrix!
I would be very interested in any evaluations regarding the performance of the smaller models to observe the impact of the imatrix in this use case.
Did you evaluate the model sizes and if so, would you consider adding this to the model card?
Since this is a highly specialized model the best route to go for any kind of meaningful score would be to port the Berkeley Function-Calling Leaderboard to llama-cpp-python and see how each quant scores against the original weights.
Unless someone else wants to do that I might look into it once issues with IQ1_S are resolved and I've requantized it.