Instructions to use meetkai/functionary-small-v2.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use meetkai/functionary-small-v2.2-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="meetkai/functionary-small-v2.2-GGUF", filename="functionary-small-v2.2.f16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use meetkai/functionary-small-v2.2-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf meetkai/functionary-small-v2.2-GGUF:F16 # Run inference directly in the terminal: llama-cli -hf meetkai/functionary-small-v2.2-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf meetkai/functionary-small-v2.2-GGUF:F16 # Run inference directly in the terminal: llama-cli -hf meetkai/functionary-small-v2.2-GGUF:F16
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 meetkai/functionary-small-v2.2-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf meetkai/functionary-small-v2.2-GGUF:F16
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 meetkai/functionary-small-v2.2-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf meetkai/functionary-small-v2.2-GGUF:F16
Use Docker
docker model run hf.co/meetkai/functionary-small-v2.2-GGUF:F16
- LM Studio
- Jan
- Ollama
How to use meetkai/functionary-small-v2.2-GGUF with Ollama:
ollama run hf.co/meetkai/functionary-small-v2.2-GGUF:F16
- Unsloth Studio new
How to use meetkai/functionary-small-v2.2-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 meetkai/functionary-small-v2.2-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 meetkai/functionary-small-v2.2-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for meetkai/functionary-small-v2.2-GGUF to start chatting
- Docker Model Runner
How to use meetkai/functionary-small-v2.2-GGUF with Docker Model Runner:
docker model run hf.co/meetkai/functionary-small-v2.2-GGUF:F16
- Lemonade
How to use meetkai/functionary-small-v2.2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull meetkai/functionary-small-v2.2-GGUF:F16
Run and chat with the model
lemonade run user.functionary-small-v2.2-GGUF-F16
List all available models
lemonade list
can not call the functions
try this::::
"messages": [
{
"content": "You are a helpful AI assistant.\n For coding tasks, prioritize using "functions" in "tools" to solve the problem, with the function call named in "name" and its parameters defined in "parameters".\n Reply "TERMINATE" in the end when everything is done.\n ",
"role": "system"
},
{
"content": "query the latest economic data and major events",
"role": "user"
}
],
"model": "gpt-3.5-turbo-1106",
"stream": false,
"tools": [
{
"type": "function",
"function": {
"description": "run cell in ipython and return the execution result.",
"name": "python",
"parameters": {
"type": "object",
"properties": {
"cell": {
"type": "string",
"description": "Valid Python cell to execute."
}
},
"required": [
"cell"
]
}
}
},
{
"type": "function",
"function": {
"description": "run a shell script and return the execution result.",
"name": "sh",
"parameters": {
"type": "object",
"properties": {
"script": {
"type": "string",
"description": "Valid Python cell to execute."
}
},
"required": [
"script"
]
}
}
},
{
"type": "function",
"function": {
"description": "query the latest economic data and major events, and return the execution results.",
"name": "economic_data_cursor",
"parameters": {
"type": "object",
"properties": {},
"required": []
}
}
}
]
}
Hi, for the GGUF models, you'll need to use llama-cpp-python for inference. You can refer to our sample code here for how to perform inference with the GGUF models and llama-cpp-python.
The integration of functionary GGUF models into llama-cpp-python's OpenAI-compatible servers is still ongoing now in this PR. Once it is integrated, you will be able to start your own server in llama-cpp-python and send in requests via OpenAI API requests. For now, you will have to perform inference normally without a server first using the provided sample code.
Thank you!