Update README.md
Browse filesAdd function calling configuration
README.md
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# OpenVINO IR model with int8 quantization of Hermes-2-Pro-Llama-3-8B
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Model definition for LocalAI:
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```
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name: hermes-2-pro-llama3
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backend: transformers
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parameters:
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use_tokenizer_template: true
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```
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# Hermes 2 Pro - Llama-3 8B
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# OpenVINO IR model with int8 quantization of Hermes-2-Pro-Llama-3-8B
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Model definition for LocalAI:
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```yaml
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name: hermes-2-pro-llama3
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backend: transformers
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parameters:
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use_tokenizer_template: true
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```
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LocalAI configuration for function calling
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```yaml
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name: hermes-2-pro-llama3
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backend: transformers
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parameters:
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model: fakezeta/Hermes-2-Pro-Llama-3-8B-ov-int8
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context_size: 8192
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type: OVModelForCausalLM
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function:
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# disable injecting the "answer" tool
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disable_no_action: true
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# This allows the grammar to also return messages
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grammar_message: true
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# Suffix to add to the grammar
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grammar_prefix: '<tool_call>\n'
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return_name_in_function_response: true
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# Without grammar uncomment the lines below
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# Warning: this is relying only on the capability of the
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# LLM model to generate the correct function call.
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# no_grammar: true
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# json_regex_match: "(?s)<tool_call>(.*?)</tool_call>"
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replace_results:
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"<tool_call>": ""
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"\'": "\""
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template:
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chat_message: |
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<|im_start|>{{if eq .RoleName "assistant"}}assistant{{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_start|>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_start|>assistant
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<tool_call>
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chat: |
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{{.Input -}}
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<|im_start|>assistant
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completion: |
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{{.Input}}
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```
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# Hermes 2 Pro - Llama-3 8B
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