ryota39
/

Phi-3-mini-4k-instruct-dpo

@@ -1,199 +1,86 @@
 ---
 library_name: transformers
-tags: []
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 ---
 library_name: transformers
+tags:
+- dpo
+license: mit
+datasets:
+- llm-jp/hh-rlhf-12k-ja
+language:
+- ja
 ---
+## モデル
+- ベースモデル：[microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
+- 学習データセット：[llm-jp/hh-rlhf-12k-ja](https://huggingface.co/datasets/llm-jp/hh-rlhf-12k-ja)
+- 学習方式：フルパラメータチューニング
+## サンプル
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained(
+    "ryota39/Phi-3-mini-4k-instruct-dpo",
+    trust_remote_code=True,
+    )
+model = AutoModelForCausalLM.from_pretrained(
+    "ryota39/Phi-3-mini-4k-instruct-dpo",
+    device_map="auto",
+    torch_dtype='auto',
+    trust_remote_code=True,
+    )
+text = "<|user|>\n与えられた質問に対して英語で思考し、日本語で答えてください。東京の観光地を教えてください。\n<|end|>\n<|assistant|>\n"
+tokenized_input = tokenizer.encode(
+    text,
+    add_special_tokens=False,
+    return_tensors="pt"
+    ).to(model.device)
+with torch.no_grad():
+    output = model.generate(
+        tokenized_input,
+        max_new_tokens=500,
+        do_sample=True,
+        top_p=0.95,
+        temperature=0.8,
+        repetition_penalty=1.0
+    )[0]
+print(tokenizer.decode(output))
+```
+## 出力例
+```
+<|user|> 与えられた質問に対して英語で思考し、日本語で答えてください。東京の観光地を教えてください。
+<|end|><|assistant|> 東京には様々な観光地がありますが、代表的なものとして以下のようなものがあります。
+1. 浅草寺（あさくさじ） - 歴史ある寺院で、浅草の様々な景観や、人々が集まる場所です。
+2. 東京タワー - 日本の象徴的な電波塔であり、東京の景色を眺めるのに最適な場所です。
+3. 皇居（こうこく） - 日本の皇族が住んでいる皇居で、日本の歴史を感じられる郊外もあります。
+4. 渋谷スクランブル交差点 - 日本のトレンドを象徴するスクランブル交差点で、最新のストリートファッションやアートを見ることができます。
+5. 渋谷の百 ár - 若者の雰囲気を感じられるショップ街で、最新のグッズを見つけることができます。
+これらは東京を象徴する一部の観光地ですが、他にもたくさんの観光地がありますので、ご興味のある場所をご確認ください。<|end|>
+```
+## 謝辞
+本成果は【LOCAL AI HACKATHON #001】240時間ハッカソンの成果です。
+運営の方々に深く御礼申し上げます。
+- 【メタデータラボ株式会社】様
+- 【AI声づくり技術研究会】
+  - サーバー主：やなぎ(Yanagi)様
+- 【ローカルLLMに向き合う会】
+  - サーバー主：saldra(サルドラ)様
+[メタデータラボ、日本最大規模のAIハッカソン「LOCAL AI HACKATHON #001」~ AIの民主化 ~を開催、本日より出場チームの募集を開始](https://prtimes.jp/main/html/rd/p/000000008.000056944.html)