--- base_model: meetkai/functionary-small-v3.1 license: mit model_creator: meetkai model_name: functionary-small-v3.1 quantized_by: Second State Inc. ---

# functionary-small-v3.1-GGUF ## Original Model [meetkai/functionary-small-v3.1](https://huggingface.co/meetkai/functionary-small-v3.1) ## Run with LlamaEdge - LlamaEdge version: coming soon - Prompt template - Prompt type: `functionary-31` - Prompt string ```text <|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 => ` a JSON dict with the function argument name as key and function argument value as value. end_tag => `` Here is an example, {"example_name": "example_value"} 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 - 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 ```bash 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 ```bash 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 ``` ## Quantized GGUF Models | Name | Quant method | Bits | Size | Use case | | ---- | ---- | ---- | ---- | ----- | | [functionary-small-v3.1-Q2_K.gguf](https://huggingface.co/second-state/functionary-small-v3.1-GGUF/blob/main/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](https://huggingface.co/second-state/functionary-small-v3.1-GGUF/blob/main/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](https://huggingface.co/second-state/functionary-small-v3.1-GGUF/blob/main/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](https://huggingface.co/second-state/functionary-small-v3.1-GGUF/blob/main/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](https://huggingface.co/second-state/functionary-small-v3.1-GGUF/blob/main/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](https://huggingface.co/second-state/functionary-small-v3.1-GGUF/blob/main/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](https://huggingface.co/second-state/functionary-small-v3.1-GGUF/blob/main/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](https://huggingface.co/second-state/functionary-small-v3.1-GGUF/blob/main/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](https://huggingface.co/second-state/functionary-small-v3.1-GGUF/blob/main/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](https://huggingface.co/second-state/functionary-small-v3.1-GGUF/blob/main/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](https://huggingface.co/second-state/functionary-small-v3.1-GGUF/blob/main/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](https://huggingface.co/second-state/functionary-small-v3.1-GGUF/blob/main/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](https://huggingface.co/second-state/functionary-small-v3.1-GGUF/blob/main/functionary-small-v3.1-f16.gguf) | f16 | 16 | 16.1 GB| | *Quantized with llama.cpp b3807*