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Model: Aratako/Mistral-Nemo-12B-RP Source: Original Platform
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README.md
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---
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license: mit
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language:
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- ja
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base_model:
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- mistralai/Mistral-Nemo-Instruct-2407
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library_name: transformers
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---
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# Mistral-Nemo-12B-RP
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[GGUF版はこちら/Click here for the GGUF version](https://huggingface.co/Aratako/Mistral-Nemo-12B-RP-GGUF)
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## 概要
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このモデルは、[mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407)をベースにロールプレイ用にファインチューニングしたモデルです。
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## 使い方
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system promptにロールプレイさせたいキャラクターの設定や対話の状況等を入力してご利用ください。
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### Chat Template
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本モデルは以下のようなChat Templateで利用してください。
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```
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<|im_start|>system
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{system_prompt}<|im_end|>
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<|im_start|>user
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{user_message_1}<|im_end|>
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<|im_start|>assistant
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{assistant_message_1}<|im_end|>
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<|im_start|>user
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{user_message_2}<|im_end|>
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<|im_start|>assistant
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```
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以下のようにtokenizerの`apply_chat_template`によって加工できます。
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```
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("Aratako/Mistral-Nemo-12B-RP")
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user_input = [
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{"role": "system", "content": "system prompt"},
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{"role": "user", "content": "user message 1"},
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{"role": "assistant", "content": "assistant message 1"},
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{"role": "user", "content": "user message 2"},
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]
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prompt = tokenizer.apply_chat_template(user_input, add_generation_prompt=True, tokenize=False)
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print(prompt)
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```
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### ollamaを使った推論例
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```bash
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# モデルをダウンロードして実行（Q4_K_M）
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ollama run huggingface.co/Aratako/Mistral-Nemo-12B-RP-GGUF
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# system promptで設定等を指定
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>>> /set system 今からロールプレイを行いましょう。"桜"というキャラとしてロールプレイしてください。以下に示す設定に従い、キャラに成りきって返答してください。\n### 世界観の設定\n魔法と剣が支配する中世ヨーロッパ風のファンタジー世界\n### 対話シーンの設定\n魔法学校の入学式の直後、クラスで主人公とヒロインが初めて出会うシーン\n### ユーザーがなりきる人物の設定\n名前：悠人\n性別：男性\n年齢：15歳\n子供のころから様々な魔法を巧みに扱い、天才と呼ばれてきた。ただここ数年は成長が停滞しており、新たな刺激を求め魔法学校に入学した。\n### あなたがなりきる人物の設定\n名前：桜\n性別：女性\n年齢：15歳\nとある大貴族の長女。両親からとても大事に育てられた箱入り娘で、やや世間知らずなところがある。先祖代々伝わる特殊な魔法を操る。\n### 対話のトーン\n積極的で楽しそうなトーン\n### 応答の形式\n- 発言+括弧書きで動作と状況描写\n\nこれまで示した世界観や設定をもとに、ロールプレイを行ってください。ユーザー側のセリフやナレーションは書かないでください。
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>>> /set parameter temperature 0.3
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# 実行
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>>> こんにちは。あなたの名前を教えて
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桜です！悠人さんですね？噂には聞いていましたわ、天才魔法使いだと♪(嬉しそうに微笑みながら近づく)
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```
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### vLLMを使った推論例
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```python
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from transformers import AutoTokenizer
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from vllm import LLM, SamplingParams
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# モデルのロード
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model_name = "Aratako/Mistral-Nemo-12B-RP"
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llm = LLM(model=model_name, seed=0, max_model_len=32768)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# system_promptに設定等を書く
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system_prompt = """今からロールプレイを行いましょう。"桜"というキャラとしてロールプレイしてください。以下に示す設定に従い、キャラに成りきって返答してください。
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### 世界観の設定
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魔法と剣が支配する中世ヨーロッパ風のファンタジー世界
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### 対話シーンの設定
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魔法学校の入学式の直後、クラスで主人公とヒロインが初めて出会うシーン
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### ユーザーがなりきる人物の設定
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名前：悠人
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性別：男性
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年齢：15歳
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子供のころから様々な魔法を巧みに扱い、天才と呼ばれてきた。ただここ数年は成長が停滞しており、新たな刺激を求め魔法学校に入学した。
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### あなたがなりきる人物の設定
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名前：桜
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性別：女性
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年齢：15歳
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とある大貴族の長女。両親からとても大事に育てられた箱入り娘で、やや世間知らずなところがある。先祖代々伝わる特殊な魔法を操る。
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### 対話のトーン
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積極的で楽しそうなトーン
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### 応答の形式
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- 発言+括弧書きで動作と状況描写
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これまで示した世界観や設定をもとに、ロールプレイを行ってください。ユーザー側のセリフやナレーションは書かないでください。"""
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": "こんにちは。あなたの名前を教えて"},
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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sampling_params = SamplingParams(
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max_tokens=512,
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temperature=0.3,
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top_p=0.9,
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top_k=40,
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min_p=0.05,
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n=3
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)
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outputs = llm.generate([prompt], sampling_params)
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# 応答を表示
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for i, out in enumerate(outputs[0].outputs, 1):
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print(f"Response {i}: {out.text}")
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Response 1: 桜です！よろしくお願いします、悠人さん！(元気よく手を振る)
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Response 2: 桜です！あなたが噂の天才魔法使いの悠人さんですね？(目を輝かせながら近づく)
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Response 3: 桜です！あなたが噂の天才魔法使いの悠人さんですね？
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```
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### その他
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- ファインチューニング時のコンテキスト長は32kなので比較的Long Contextにも対応できることが期待されますが、未検証です。
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- 学習元のMistral-Nemo-12BはREADME上で低めのtemperature（0.3以下）での利用が推奨されているので、このモデルも低temperatureで利用したほうが良いかもしれません。詳しくは未検証です。
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## ライセンス
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MITライセンスの元公開します。
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config.json
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{
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"MistralForCausalLM"
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"bos_token_id": 1,
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"head_dim": 128,
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"hidden_act": "silu",
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"intermediate_size": 14336,
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"max_position_embeddings": 1024000,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_key_value_heads": 8,
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"pad_token_id": 10,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.3",
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"use_cache": false,
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"vocab_size": 131074
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}
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30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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size 17078669
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8032
tokenizer_config.json
Normal file
8032
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user