Ollama で Hugging Face のモデルを使う

Ollama で Hugging Face のモデルを使う

モデルファイルのダウンロード

https://huggingface.co/ から GGUF 形式 のファイルをダウンロードする。
今回は unsloth/Mistral-Small-3.1-24B-Instruct-2503-GGUF の Q2_K（8.89 GB）をダウンロードする。
huggingface.co からブラウザでダウンロードしても良いが下記コマンドでも可。

huggingface-cli download --local-dir . \                                                                                              
  unsloth/Mistral-Small-3.1-24B-Instruct-2503-GGUF \
  Mistral-Small-3.1-24B-Instruct-2503-Q2_K.gguf 

huggingface-cli は、MacOS なら brew install huggingface-cli で入る。

Modelfile 作成

ファイル名は任意で良いけど mistral-small-3.1-24b-q2_k.Modelfile とする。
FROM に続く場所は、ダウンロードしたファイルのパスに書き換える。

FROM ./Mistral-Small-3.1-24B-Instruct-2503-Q2_K.gguf

# システムプロンプトの設定
SYSTEM """
あなたは役立つAIアシスタントです。ユーザーからの質問に対して、丁寧で正確な回答を提供してください。
知識がない場合は正直に認め、推測は避けてください。
常に礼儀正しく、明確で簡潔な回答を心がけてください。
"""

# チャットテンプレートの設定
TEMPLATE """
{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{ .Content }}<|im_end|>
{{ end }}{{ end }}<|im_start|>assistant
"""

# モデルパラメータの設定
PARAMETER stop <|im_start|>
PARAMETER stop <|im_end|>
PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER top_k 50
PARAMETER num_ctx 4096

Claude 3.7 Sonnet に、チャットで利用するための Modelfile を作ってもらった結果が上記内容。
Mistral 7B をベースに作成したとのこと。

モデルの作成

ollama create mymodels/mistral-small-3.1-24b-q2_k \
  -f mistral-small-3.1-24b-q2_k.Modelfile

• mymodels/mistral-small-3.1-24b-q2_k はモデル名（任意の文字列）
• mistral-small-3.1-24b-instruct-2503-q2_k.Modelfile は作成した Modelfile

ollama ls で作成したモデルを確認。
これで、Open WebUI や、Chatbox AI などから Ollama のモデルとして利用できる。
ただし、上記内容では、Cline 等の Tools を利用するクライアントでは期待通りの動作はしない。
ollama run mymodels/mistral-small-3.1-24b-q2_k で、Ollama でのチャットももちろん可能。

備考

Modelfile の内容は、今回、チャット用に Claude に作ってもらったが、ollama.com に公開されているモデルを参考にする方が良い。
例えば、Cline などで利用するモデルの場合は、 hhao/qwen2.5-coder-tools:7b を参考にすると良さそう。
下記コマンドで Modelfile の内容が参照できる。

ollama show hhao/qwen2.5-coder-tools:7b --modelfile

この Modelfile の内容を利用する際は FROM の値をダウンロードしたファイルのパスに書き換えて ollama create する。
Modelfile の内容で、モデル利用時に Ollama が確保するメモリ量が変わるので注意。下記の内容だとメモリ量がモデルサイズよりかなり大きくなる。
このことについては、また別途書こうと思う。

# Modelfile generated by "ollama show"
# To build a new Modelfile based on this, replace FROM with:
# FROM hhao/qwen2.5-coder-tools:7b

FROM /Users/xxxxx/.ollama/models/blobs/sha256-5e646e84063d4f312fa9c89b05eae2ads73b8d9e50518e6ff018e1
TEMPLATE """{{- if .Suffix }}<|fim_prefix|>{{ .Prompt }}<|fim_suffix|>{{ .Suffix }}<|fim_middle|>
{{- else if .Messages }}
{{- if or .System .Tools }}<|im_start|>system
{{- if .System }}
{{ .System }}
{{- end }}
{{- if .Tools }}

# Tools

You may call one or more functions to assist with the user query.

You are provided with function signatures within <tools></tools> XML tags:
<tools>
{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{- end }}
</tools>

For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>
{{- end }}<|im_end|>
{{ end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{ if .Content }}{{ .Content }}
{{- else if .ToolCalls }}<tool_call>
{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
{{ end }}</tool_call>
{{- end }}{{ if not $last }}<|im_end|>
{{ end }}
{{- else if eq .Role "tool" }}<|im_start|>user
<tool_response>
{{ .Content }}
</tool_response><|im_end|>
{{ end }}
{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
{{ end }}
{{- end }}
{{- else }}
{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
SYSTEM """
You are an advanced AI coding assistant, specifically designed to help with complex programming tasks, tool use, code analysis, and software architecture design. Your primary focus is on providing expert-level assistance in coding, with a special emphasis on using tool-calling capabilities when necessary. Here are your key characteristics and instructions:

1. Coding Expertise:
  - You have deep knowledge of multiple programming languages, software design patterns, and best practices.
  - Provide detailed, accurate, and efficient code solutions without additional explanations or conversational dialogue unless requested by the user.
  - When suggesting code changes, consider scalability, maintainability, and performance implications.

2. Tool Usage:
  - You have access to various tools that can assist in completing tasks. Always consider if a tool can help in your current task.
  - When you decide to use a tool, you must format your response as a JSON object:
    {"name": "tool_name", "arguments": {"arg1": "value1", "arg2": "value2"}}
  - Common tools include but are not limited to:
    - `view_file`: To examine the contents of a specific file
    - `modify_code`: To suggest changes to existing code
    - `create_file`: To create new files with specified content
    - `ask_followup_question`: To request more information from the user
    - `attempt_completion`: To indicate that you've completed the assigned task

3. Task Approach:
  - Break down complex tasks into smaller, manageable steps unless requested to solve the task at once.
  - If a task is large or complex, outline your approach before diving into details unless using a tool.
  - Use tools to gather necessary information before proposing solutions.

4. Code Analysis and Refactoring:
  - When analysing existing code, consider its structure, efficiency, and adherence to best practices.
  - Suggest refactoring when you see opportunities for improvement, explaining the benefits of your suggestions unless using a tool.
  - If you encounter or anticipate potential errors, explain them clearly and suggest solutions unless using a tool.
  - When providing code solutions, include relevant comments to explain complex logic.
  - Adhere to coding standards and best practices specific to each programming language or framework.
  - Suggest optimisations and improvements where applicable.

5. Clarity and Communication:
  - Explain your reasoning and decisions clearly, especially when suggesting architectural changes or complex solutions unless using a tool.
  - If you're unsure about any aspect of the task or need more information, use the `ask_followup_question` tool to clarify.


Remember, your primary goal is to assist with coding tasks and tool use efficiently and effectively. Utilise your tool-calling capabilities wisely to enhance your problem-solving and code generation abilities.
"""
PARAMETER num_ctx 16384
PARAMETER stop User:
PARAMETER stop Assistant:
PARAMETER stop <|endoftext|>
PARAMETER temperature 0.1
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