---

functionary-medium-v3.1-GGUF

Original Model

meetkai/functionary-medium-v3.1

Run with LlamaEdge

• 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-medium-v3.1-Q5_K_M.gguf \
    llama-api-server.wasm \
    --model-name functionary-medium-v3.1 \
    --prompt-template functionary-31 \
    --ctx-size 128000
  

• Run as LlamaEdge command app

  wasmedge --dir .:. --nn-preload default:GGML:AUTO:functionary-medium-v3.1-Q5_K_M.gguf \
    llama-chat.wasm \
    --prompt-template functionary-31 \
    --ctx-size 128000
  

Quantized GGUF Models

Name	Quant method	Bits	Size	Use case
functionary-medium-v3.1-Q2_K.gguf	Q2_K	2	26.4 GB	smallest, significant quality loss - not recommended for most purposes
functionary-medium-v3.1-Q3_K_L.gguf	Q3_K_L	3	37.1 GB	small, substantial quality loss
functionary-medium-v3.1-Q3_K_M.gguf	Q3_K_M	3	34.3 GB	very small, high quality loss
functionary-medium-v3.1-Q3_K_S.gguf	Q3_K_S	3	30.9 GB	very small, high quality loss
functionary-medium-v3.1-Q4_0.gguf	Q4_0	4	40.0 GB	legacy; small, very high quality loss - prefer using Q3_K_M
functionary-medium-v3.1-Q4_K_M.gguf	Q4_K_M	4	42.5 GB	medium, balanced quality - recommended
functionary-medium-v3.1-Q4_K_S.gguf	Q4_K_S	4	40.3 GB	small, greater quality loss
functionary-medium-v3.1-Q5_0.gguf	Q5_0	5	48.7 GB	legacy; medium, balanced quality - prefer using Q4_K_M
functionary-medium-v3.1-Q5_K_M.gguf	Q5_K_M	5	49.9 GB	large, very low quality loss - recommended
functionary-medium-v3.1-Q5_K_S.gguf	Q5_K_S	5	48.7 GB	large, low quality loss - recommended
functionary-medium-v3.1-Q6_K-00001-of-00002.gguf	Q6_K	6	29.8 GB	very large, extremely low quality loss
functionary-medium-v3.1-Q6_K-00002-of-00002.gguf	Q6_K	6	28.0 GB	very large, extremely low quality loss
functionary-medium-v3.1-Q8_0-00001-of-00003.gguf	Q8_0	8	29.8 GB	very large, extremely low quality loss - not recommended
functionary-medium-v3.1-Q8_0-00002-of-00003.gguf	Q8_0	8	29.8 GB	very large, extremely low quality loss - not recommended
functionary-medium-v3.1-Q8_0-00003-of-00003.gguf	Q8_0	8	15.4 GB	very large, extremely low quality loss - not recommended
functionary-medium-v3.1-f16-00001-of-00005.gguf	f16	16	30.0 GB
functionary-medium-v3.1-f16-00002-of-00005.gguf	f16	16	29.6 GB
functionary-medium-v3.1-f16-00003-of-00005.gguf	f16	16	29.9 GB
functionary-medium-v3.1-f16-00004-of-00005.gguf	f16	16	29.6 GB
functionary-medium-v3.1-f16-00005-of-00005.gguf	f16	16	22.2 GB

Quantized with llama.cpp b3807