1. 下载 gguf 文件

使用浏览器打开 https://huggingface.co/mmnga/Llama-3.1-70B-Japanese-Instruct-2407-gguf，下载你想使用的量化版本。

例如，Llama-3.1-70B-Japanese-Instruct-2407-Q4_K_M.gguf，

2. 创建 Modelfile

创建 Modelfile，内容如下，

# ollama create llama3.1:70b-japanese-tool-q4_K_M -f ./Modelfile

FROM ./Llama-3.1-70B-Japanese-Instruct-2407-Q4_K_M.gguf
TEMPLATE "{{ if .Messages }}
{{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|>
{{- if .System }}

{{ .System }}
{{- end }}
{{- if .Tools }}

You are a helpful assistant with tool calling capabilities. When you receive a tool call response, use the output to format an answer to the orginal use question.
{{- end }}<|eot_id|>
{{- end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 }}
{{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|>
{{- if and $.Tools $last }}

Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.

Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables.

{{ $.Tools }}
{{- end }}

{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>

{{ end }}
{{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|>
{{- if .ToolCalls }}

{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}{{ end }}
{{- else }}

{{ .Content }}{{ if not $last }}<|eot_id|>{{ end }}
{{- end }}
{{- else if eq .Role "tool" }}<|start_header_id|>ipython<|end_header_id|>

{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>

{{ end }}
{{- end }}
{{- end }}
{{- else }}
{{- if .System }}<|start_header_id|>system<|end_header_id|>

{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>

{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>

{{ end }}{{ .Response }}{{ if .Response }}<|eot_id|>{{ end }}
"
PARAMETER stop <|start_header_id|>
PARAMETER stop <|end_header_id|>
PARAMETER stop <|eot_id|>

3. 创建 Ollama 模型

运行下面命令创建 Ollama 模型，

ollama create llama3.1:70b-japanese-tool-q4_K_M -f ./Modelfile

4. 运行 Ollama 模型

ollama run llama3.1:70b-japanese-tool-q4_K_M

5. （可选）其他 Modelfile

下面是不支持执行 tool 的 Modelfile。

FROM ./Llama-3.1-70B-Japanese-Instruct-2407-Q4_K_M.gguf
TEMPLATE "{{- if .Messages }}
{{- if .System }}<|start_header_id|>system<|end_header_id|>

{{ .System }}<|eot_id|>
{{- end }}
{{- range .Messages }}<|start_header_id|>{{ .Role }}<|end_header_id|>

{{ .Content }}<|eot_id|>
{{- end }}<|start_header_id|>assistant<|end_header_id|>

{{ else }}
{{- if .System }}<|start_header_id|>system<|end_header_id|>

{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>

{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>

{{ end }}{{ .Response }}{{ if .Response }}<|eot_id|>{{ end }}"
PARAMETER stop <|start_header_id|>
PARAMETER stop <|end_header_id|>
PARAMETER stop <|eot_id|>

完结！