name: gpt-4
mmap: false
f16: false
parameters:
model: huggingface://NousResearch/Hermes-2-Pro-Mistral-7B-GGUF/Hermes-2-Pro-Mistral-7B.Q6_K.gguf
roles:
assistant_function_call: assistant
function: tool
template:
chat_message: |
<|im_start|>{{if eq .RoleName "assistant"}}assistant{{else if eq .RoleName "system"}}system{{else if eq .RoleName "function"}}{{.Role}}{{else if eq .RoleName "user"}}user{{end}}
{{ if eq .RoleName "assistant_function_call" }}{{end}}
{{ if eq .RoleName "function" }}{{end}}
{{if .Content}}{{.Content}}{{end}}
{{if .FunctionCall}}{{toJson .FunctionCall}}{{end}}
{{ if eq .RoleName "assistant_function_call" }}{{end}}
{{ if eq .RoleName "function" }}{{end}}
<|im_end|>
# https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF#prompt-format-for-function-calling
function: |
<|im_start|>system
You are a function calling AI model. You are provided with function signatures within 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:
{{range .Functions}}
{'type': 'function', 'function': {'name': '{{.Name}}', 'description': '{{.Description}}', 'parameters': {{toJson .Parameters}} }}
{{end}}
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']}
For each function call return a json object with function name and arguments within XML tags as follows:
{'arguments': , 'name': }
<|im_end|>
{{.Input}}
<|im_start|>assistant
chat: |
{{.Input}}
<|im_start|>assistant
completion: |
{{.Input}}
context_size: 4096
stopwords:
- <|im_end|>
-
usage: |
curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "gpt-4",
"messages": [{"role": "user", "content": "How are you doing?", "temperature": 0.1}]
}'