Instructions to use second-state/functionary-small-v3.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use second-state/functionary-small-v3.2-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="second-state/functionary-small-v3.2-GGUF", filename="functionary-small-v3.2-Q2_K.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 second-state/functionary-small-v3.2-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf second-state/functionary-small-v3.2-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf second-state/functionary-small-v3.2-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf second-state/functionary-small-v3.2-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf second-state/functionary-small-v3.2-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 second-state/functionary-small-v3.2-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf second-state/functionary-small-v3.2-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 second-state/functionary-small-v3.2-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf second-state/functionary-small-v3.2-GGUF:Q4_K_M
Use Docker
docker model run hf.co/second-state/functionary-small-v3.2-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use second-state/functionary-small-v3.2-GGUF with Ollama:
ollama run hf.co/second-state/functionary-small-v3.2-GGUF:Q4_K_M
- Unsloth Studio new
How to use second-state/functionary-small-v3.2-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 second-state/functionary-small-v3.2-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 second-state/functionary-small-v3.2-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for second-state/functionary-small-v3.2-GGUF to start chatting
- Pi new
How to use second-state/functionary-small-v3.2-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf second-state/functionary-small-v3.2-GGUF:Q4_K_M
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": "second-state/functionary-small-v3.2-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use second-state/functionary-small-v3.2-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 second-state/functionary-small-v3.2-GGUF:Q4_K_M
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 second-state/functionary-small-v3.2-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use second-state/functionary-small-v3.2-GGUF with Docker Model Runner:
docker model run hf.co/second-state/functionary-small-v3.2-GGUF:Q4_K_M
- Lemonade
How to use second-state/functionary-small-v3.2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull second-state/functionary-small-v3.2-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.functionary-small-v3.2-GGUF-Q4_K_M
List all available models
lemonade list
functionary-small-v3.2-GGUF
Original Model
meetkai/functionary-small-v3.2
Run with LlamaEdge
LlamaEdge version: v0.14.10 and above
Prompt template
Prompt type:
functionary-32Prompt string
<|start_header_id|>system<|end_header_id|> You are capable of executing available function(s) if required. Only execute function(s) when absolutely necessary. Ask for the required input to:recipient==all Use JSON for function arguments. Respond in this format: >>>${recipient} ${content} Available functions: // Supported function definitions that should be called when necessary. namespace functions { // Get the current weather type get_current_weather = (_: { // The city and state, e.g. San Francisco, CA location: string, }) => any; } // namespace functions<|eot_id|><|start_header_id|>user<|end_header_id|> What is the weather like in Beijing today?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Context size:
128000Run as LlamaEdge service
wasmedge --dir .:. --nn-preload default:GGML:AUTO:functionary-small-v3.2-Q5_K_M.gguf \ llama-api-server.wasm \ --model-name functionary-small-v3.2 \ --prompt-template functionary-32 \ --ctx-size 128000Run as LlamaEdge command app
wasmedge --dir .:. --nn-preload default:GGML:AUTO:functionary-small-v3.2-Q5_K_M.gguf \ llama-chat.wasm \ --prompt-template functionary-32 \ --ctx-size 128000
Quantized GGUF Models
| Name | Quant method | Bits | Size | Use case |
|---|---|---|---|---|
| functionary-small-v3.2-Q2_K.gguf | Q2_K | 2 | 3.18 GB | smallest, significant quality loss - not recommended for most purposes |
| functionary-small-v3.2-Q3_K_L.gguf | Q3_K_L | 3 | 4.32 GB | small, substantial quality loss |
| functionary-small-v3.2-Q3_K_M.gguf | Q3_K_M | 3 | 4.02 GB | very small, high quality loss |
| functionary-small-v3.2-Q3_K_S.gguf | Q3_K_S | 3 | 3.66 GB | very small, high quality loss |
| functionary-small-v3.2-Q4_0.gguf | Q4_0 | 4 | 4.66 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| functionary-small-v3.2-Q4_K_M.gguf | Q4_K_M | 4 | 4.92 GB | medium, balanced quality - recommended |
| functionary-small-v3.2-Q4_K_S.gguf | Q4_K_S | 4 | 4.69 GB | small, greater quality loss |
| functionary-small-v3.2-Q5_0.gguf | Q5_0 | 5 | 5.60 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| functionary-small-v3.2-Q5_K_M.gguf | Q5_K_M | 5 | 5.73 GB | large, very low quality loss - recommended |
| functionary-small-v3.2-Q5_K_S.gguf | Q5_K_S | 5 | 5.60 GB | large, low quality loss - recommended |
| functionary-small-v3.2-Q6_K.gguf | Q6_K | 6 | 6.60 GB | very large, extremely low quality loss |
| functionary-small-v3.2-Q8_0.gguf | Q8_0 | 8 | 8.54 GB | very large, extremely low quality loss - not recommended |
| functionary-small-v3.2-f16.gguf | f16 | 16 | 16.1 GB |
Quantized with llama.cpp b3807
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Model tree for second-state/functionary-small-v3.2-GGUF
Base model
meetkai/functionary-small-v3.2