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54 lines
2.0 KiB
YAML
54 lines
2.0 KiB
YAML
---
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config_file: |
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mmap: true
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backend: llama-cpp
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template:
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chat_message: |
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<|im_{{if eq .RoleName "assistant"}}bot{{else if eq .RoleName "system"}}system{{else if eq .RoleName "tool"}}tool{{else if eq .RoleName "user"}}user{{end}}|>
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{{- if .FunctionCall }}
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<tool_call>
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{{- else if eq .RoleName "tool" }}
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<tool_response>
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{{- end }}
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{{- if .Content}}
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{{.Content }}
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{{- end }}
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{{- if .FunctionCall}}
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{{toJson .FunctionCall}}
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{{- end }}
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{{- if .FunctionCall }}
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</tool_call>
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{{- else if eq .RoleName "tool" }}
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</tool_response>
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{{- end }}<|im_end|>
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# https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF#prompt-format-for-function-calling
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function: |
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<|im_system|>
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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:
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<tools>
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{{range .Functions}}
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{'type': 'function', 'function': {'name': '{{.Name}}', 'description': '{{.Description}}', 'parameters': {{toJson .Parameters}} }}
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{{end}}
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</tools>
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Use the following pydantic model json schema for each tool call you will make:
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{'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}
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For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
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<tool_call>
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{'arguments': <args-dict>, 'name': <function-name>}
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</tool_call><|im_end|>
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{{.Input -}}
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<|im_bot|>
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<tool_call>
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chat: |
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{{.Input -}}
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<|im_bot|>
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completion: |
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{{.Input}}
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context_size: 4096
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f16: true
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stopwords:
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- <|im_end|>
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- <dummy32000>
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- "\n</tool_call>"
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- "\n\n\n"
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