--- name: "deephermes" config_file: | mmap: true context_size: 8192 stopwords: - "<|im_end|>" - "<dummy32000>" - "<|eot_id|>" - "<|end_of_text|>" function: disable_no_action: true grammar: triggers: - word: "<tool_call>" at_start: false template: chat_message: | <|start_header_id|>{{if eq .RoleName "assistant"}}assistant{{else if eq .RoleName "system"}}system{{else if eq .RoleName "tool"}}tool{{else if eq .RoleName "user"}}user{{end}}<|end_header_id|> {{ if .FunctionCall -}} <tool_call> {{ else if eq .RoleName "tool" -}} <tool_response> {{ end -}} {{ if .Content -}} {{.Content -}} </tool_response> {{ else if .FunctionCall -}} {{ toJson .FunctionCall -}} </tool_call> {{ end -}} <|eot_id|> function: | <|start_header_id|>system<|end_header_id|> You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {{range .Functions}} {{toJson .}} {{end}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call> {"arguments": <args-dict>, "name": <function-name>} </tool_call><|eot_id|>{{.Input }} <|start_header_id|>assistant<|end_header_id|> chat: | {{.Input }} <|start_header_id|>assistant<|end_header_id|> completion: | {{.Input}}