llama3-instruct.yaml

---
name: "llama3-instruct"

config_file: |
  mmap: true
  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 -}}
      Function call:
      {{ else if eq .RoleName "tool" -}}
      Function response:
      {{ end -}}
      {{ if .Content -}}
      {{.Content -}}
      {{ else if .FunctionCall -}}
      {{ toJson .FunctionCall -}}
      {{ 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}}
      {'type': 'function', 'function': {'name': '{{.Name}}', 'description': '{{.Description}}', 'parameters': {{toJson .Parameters}} }}
      {{end}}
      </tools>
      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']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
      Function call:
    chat: |
      {{.Input }}
      <|start_header_id|>assistant<|end_header_id|>
    completion: |
      {{.Input}}
  context_size: 8192
  f16: true
  stopwords:
  - <|im_end|>
  - <dummy32000>
  - "<|eot_id|>"
  - <|end_of_text|>