Tool Use
Collection
LlamaEdge compatible quants for tool-use models. • 11 items • Updated
How to use second-state/functionary-small-v3.1-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf second-state/functionary-small-v3.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf second-state/functionary-small-v3.1-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf second-state/functionary-small-v3.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf second-state/functionary-small-v3.1-GGUF:Q4_K_M
# 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-v3.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf second-state/functionary-small-v3.1-GGUF:Q4_K_M
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-v3.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf second-state/functionary-small-v3.1-GGUF:Q4_K_M
docker model run hf.co/second-state/functionary-small-v3.1-GGUF:Q4_K_M
How to use second-state/functionary-small-v3.1-GGUF with Ollama:
ollama run hf.co/second-state/functionary-small-v3.1-GGUF:Q4_K_M
How to use second-state/functionary-small-v3.1-GGUF with Pi:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf second-state/functionary-small-v3.1-GGUF:Q4_K_M
# Install Pi:
npm install -g @earendil-works/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-v3.1-GGUF:Q4_K_M"
}
]
}
}
}# Start Pi in your project directory: pi
How to use second-state/functionary-small-v3.1-GGUF with Docker Model Runner:
docker model run hf.co/second-state/functionary-small-v3.1-GGUF:Q4_K_M
How to use second-state/functionary-small-v3.1-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull second-state/functionary-small-v3.1-GGUF:Q4_K_M
lemonade run user.functionary-small-v3.1-GGUF-Q4_K_M
lemonade list
How to use second-state/functionary-small-v3.1-GGUF with Hermes Agent:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf second-state/functionary-small-v3.1-GGUF:Q4_K_M
# 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-v3.1-GGUF:Q4_K_M
hermes
How to use second-state/functionary-small-v3.1-GGUF with OpenClaw:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf second-state/functionary-small-v3.1-GGUF:Q4_K_M
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "second-state/functionary-small-v3.1-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
openclaw agent --local --agent main --message "Hello from Hugging Face"
meetkai/functionary-small-v3.1
LlamaEdge version: v0.14.10 and above
Prompt template
Prompt type: functionary-31
Prompt string
<|start_header_id|>system<|end_header_id|>
Environment: ipython
Cutting Knowledge Date: December 2023
You have access to the following functions:
Use the function 'get_current_weather' to 'Get the current weather'
{"name":"get_current_weather","description":"Get the current weather","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"}},"required":["location"]}}
Think very carefully before calling functions.
If a you choose to call a function ONLY reply in the following format:
<{start_tag}={function_name}>{parameters}{end_tag}
where
start_tag => `<function`
parameters => a JSON dict with the function argument name as key and function argument value as value.
end_tag => `</function>`
Here is an example,
<function=example_function_name>{"example_name": "example_value"}</function>
Reminder:
- If looking for real time information use relevant functions before falling back to brave_search
- Function calls MUST follow the specified format, start with <function= and end with </function>
- Required parameters MUST be specified
- Only call one function at a time
- Put the entire function call reply on one line
<|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: 128000
Run as LlamaEdge service
wasmedge --dir .:. --nn-preload default:GGML:AUTO:functionary-small-v3.1-Q5_K_M.gguf \
llama-api-server.wasm \
--model-name functionary-small-v3.1 \
--prompt-template functionary-31 \
--ctx-size 128000
Run as LlamaEdge command app
wasmedge --dir .:. --nn-preload default:GGML:AUTO:functionary-small-v3.1-Q5_K_M.gguf \
llama-chat.wasm \
--prompt-template functionary-31 \
--ctx-size 128000
| Name | Quant method | Bits | Size | Use case |
|---|---|---|---|---|
| functionary-small-v3.1-Q2_K.gguf | Q2_K | 2 | 3.18 GB | smallest, significant quality loss - not recommended for most purposes |
| functionary-small-v3.1-Q3_K_L.gguf | Q3_K_L | 3 | 4.32 GB | small, substantial quality loss |
| functionary-small-v3.1-Q3_K_M.gguf | Q3_K_M | 3 | 4.02 GB | very small, high quality loss |
| functionary-small-v3.1-Q3_K_S.gguf | Q3_K_S | 3 | 3.66 GB | very small, high quality loss |
| functionary-small-v3.1-Q4_0.gguf | Q4_0 | 4 | 4.66 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| functionary-small-v3.1-Q4_K_M.gguf | Q4_K_M | 4 | 4.92 GB | medium, balanced quality - recommended |
| functionary-small-v3.1-Q4_K_S.gguf | Q4_K_S | 4 | 4.69 GB | small, greater quality loss |
| functionary-small-v3.1-Q5_0.gguf | Q5_0 | 5 | 5.60 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| functionary-small-v3.1-Q5_K_M.gguf | Q5_K_M | 5 | 5.73 GB | large, very low quality loss - recommended |
| functionary-small-v3.1-Q5_K_S.gguf | Q5_K_S | 5 | 5.60 GB | large, low quality loss - recommended |
| functionary-small-v3.1-Q6_K.gguf | Q6_K | 6 | 6.60 GB | very large, extremely low quality loss |
| functionary-small-v3.1-Q8_0.gguf | Q8_0 | 8 | 8.54 GB | very large, extremely low quality loss - not recommended |
| functionary-small-v3.1-f16.gguf | f16 | 16 | 16.1 GB |
Quantized with llama.cpp b3807
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Base model
meetkai/functionary-small-v3.1