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45 lines
1.7 KiB
YAML
45 lines
1.7 KiB
YAML
---
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name: "llama3-instruct"
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config_file: |
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mmap: true
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template:
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chat_message: |
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<|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|>
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{{ if .FunctionCall -}}
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Function call:
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{{ else if eq .RoleName "tool" -}}
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Function response:
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{{ end -}}
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{{ if .Content -}}
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{{.Content -}}
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{{ else if .FunctionCall -}}
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{{ toJson .FunctionCall -}}
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{{ end -}}
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<|eot_id|>
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function: |
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<|start_header_id|>system<|end_header_id|>
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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']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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Function call:
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chat: |
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{{.Input }}
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<|start_header_id|>assistant<|end_header_id|>
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completion: |
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{{.Input}}
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context_size: 8192
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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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- "<|eot_id|>"
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- <|end_of_text|>
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