name: hermes-2-pro-mistral mmap: true parameters: model: huggingface://NousResearch/Hermes-2-Pro-Mistral-7B-GGUF/Hermes-2-Pro-Mistral-7B.Q6_K.gguf roles: assistant_function_call: assistant function: tool template: chat_message: | <|im_start|>{{if eq .RoleName "assistant"}}assistant{{else if eq .RoleName "system"}}system{{else if eq .RoleName "function"}}{{.Role}}{{else if eq .RoleName "user"}}user{{end}} {{ if eq .RoleName "assistant_function_call" }}{{end}} {{ if eq .RoleName "function" }}{{end}} {{if .Content}}{{.Content}}{{end}} {{if .FunctionCall}}{{toJson .FunctionCall}}{{end}} {{ if eq .RoleName "assistant_function_call" }}{{end}} {{ if eq .RoleName "function" }}{{end}} <|im_end|> # https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF#prompt-format-for-function-calling function: | <|im_start|>system You are a function calling AI model. You are provided with function signatures within 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: {{range .Functions}} {'type': 'function', 'function': {'name': '{{.Name}}', 'description': '{{.Description}}', 'parameters': {{toJson .Parameters}} }} {{end}} Use the following pydantic model json schema for each tool call you will make: {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']} For each function call return a json object with function name and arguments within XML tags as follows: {'arguments': , 'name': } <|im_end|> {{.Input}} <|im_start|>assistant chat: | {{.Input}} <|im_start|>assistant completion: | {{.Input}} context_size: 4096 f16: true stopwords: - <|im_end|> - usage: | curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "hermes-2-pro-mistral", "messages": [{"role": "user", "content": "How are you doing?", "temperature": 0.1}] }'