eyepyon
/

rc4rinna-gpt2-medium-finetuned

@@ -1,199 +1,147 @@
 ---
 library_name: transformers
-tags: []
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-# Model Card for Model ID
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 ---
+language:
+- ja
+- en
 library_name: transformers
+pipeline_tag: text-generation
+tags:
+- fine-tuned
+- japanese-gpt2-medium
+- conversational
+- japanese
+- merged
+license: mit
+base_model: rinna/japanese-gpt2-medium
+datasets:
+- custom
+metrics:
+- perplexity
 ---
+# rc4rinna-gpt2-medium-finetuned
+## モデル概要
+このモデルは[rinna/japanese-gpt2-medium](rinna/japanese-gpt2-medium)をベースとしたファインチューニング済みモデルです。
+- **ベースモデル**: rinna/japanese-gpt2-medium
+- **モデルタイプ**: Fine-tuned Model
+- **言語**: 日本語、英語
+- **ライセンス**: MIT
+- **訓練日時**: 2025-06-12 03:40:14
+## 訓練詳細
+### データセット
+- **データセットファイル**: constitution_chat.jsonl
+- **サンプル数**: 60 件
+- **最大トークン長**: 1024
+- **データ形式**: JSONL (対話形式)
+### 訓練パラメータ
+- **訓練可能パラメータ**: 12,582,912 個
+- **総パラメータ**: 348,710,912 個
+- **訓練可能割合**: 3.6084%
+- **エポック数**: 3
+- **バッチサイズ**: 4
+- **学習率**: 5e-05
+- **データセットサイズ**: 60 サンプル
+- **訓練時間**: 0:00:08.025371
+### LoRA設定（該当する場合）
+- **LoRA Rank (r)**: 32
+- **LoRA Alpha**: 64
+- **LoRA Dropout**: 0.1
+- **対象モジュール**: c_attn, c_proj, c_fc
+## 使用方法
+### 直接使用（マージ済みモデル）
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+# モデルとトークナイザーを読み込み
+model = AutoModelForCausalLM.from_pretrained(
+    "eyepyon/rc4rinna-gpt2-medium-finetuned",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("eyepyon/rc4rinna-gpt2-medium-finetuned")
+# 推論の実行
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=512,
+            temperature=0.7,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response[len(prompt):]
+# 使用例
+prompt = "Human: こんにちは！\n\nAssistant: "
+response = generate_response(prompt)
+print(response)
+```
+### Ollamaでの使用
+```bash
+# モデルをダウンロード
+ollama pull eyepyon/rc4rinna-gpt2-medium-finetuned
+# チャット開始
+ollama run eyepyon/rc4rinna-gpt2-medium-finetuned
+```
+## パフォーマンス
+このモデルは以下のタスクに特化して訓練されています：
+- 質問応答
+- 対話生成
+- テキスト生成
+## 制限事項
+- このモデルは特定のドメインでファインチューニングされているため、汎用的な用途には適さない場合があります
+- 生成されるテキストの正確性については、使用前に検証することを推奨します
+- バイアスが含まれる可能性があります
+## 倫理的考慮事項
+- このモデルの出力は教育・研究目的での使用を想定しています
+- 有害なコンテンツの生成を避けるため、適切なフィルタリングを実装することを推奨します
+- 商用利用の際は、出力内容について十分な検証を行ってください
+## 引用
+```bibtex
+@misc{rc4rinna_gpt2_medium_finetuned,
+  title={rc4rinna-gpt2-medium-finetuned},
+  author={Your Name},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/eyepyon/rc4rinna-gpt2-medium-finetuned}
+}
+```
+## 謝辞
+- ベースモデル: [rinna/japanese-gpt2-medium](https://huggingface.co/rinna/japanese-gpt2-medium)
+- LoRA実装: [PEFT](https://github.com/huggingface/peft)
+- 訓練フレームワーク: [Transformers](https://github.com/huggingface/transformers)
+## 更新履歴
+- **v1.0** (2025-06-12): 初回リリース
+## お問い合わせ
+モデルに関する質問や改善提案がございましたら、リポジトリのIssueまでお気軽にご連絡ください。