---
name: "qwen3"

config_file: |
  mmap: true
  backend: "llama-cpp"
  template:
    chat_message: |
      <|im_start|>{{if eq .RoleName "tool" }}user{{else}}{{ .RoleName }}{{end}}
      {{ if eq .RoleName "tool" -}}
      <tool_response>
      {{ end -}}
      {{ if .Content -}}
      {{.Content }}
      {{ end -}}
      {{ if eq .RoleName "tool" -}}
      </tool_response>
      {{ end -}}
      {{ if .FunctionCall -}}
      <tool_call>
      {{toJson .FunctionCall}}
      </tool_call>
      {{ end -}}<|im_end|>
    function: |
      <|im_start|>system
      You are a function calling AI model. You are provided with functions to execute. 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}}
      For each function call return a json object with function name and arguments: {"name": <function-name>, "arguments": <json-arguments-object>}
      <|im_end|>
      {{.Input -}}
      <|im_start|>assistant
    chat: |
      {{.Input -}}
      <|im_start|>assistant
    completion: |
      {{.Input}}
  context_size: 8192
  f16: true
  stopwords:
  - '<|im_end|>'
  - '<dummy32000>'
  - '</s>'
  - '<|endoftext|>'
