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chore(model gallery): add nousresearch_deephermes-3-llama-3-8b-preview (#4828)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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gallery/deephermes.yaml
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59
gallery/deephermes.yaml
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---
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name: "deephermes"
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config_file: |
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mmap: true
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context_size: 8192
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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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function:
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disable_no_action: true
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grammar:
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triggers:
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word: "<tool_call>"
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at_start: false
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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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<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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</tool_response>
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{{ else if .FunctionCall -}}
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{{ toJson .FunctionCall -}}
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</tool_call>
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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.
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Here are the available tools:
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<tools>
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{{range .Functions}}
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{{toJson .}}
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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: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"}
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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><|eot_id|>{{.Input }}
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<|start_header_id|>assistant<|end_header_id|>
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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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