初始化项目，由ModelHub XC社区提供模型

Model: AcroYAMALEX/acro-yamalex-llmjp-4-math-cot
Source: Original Platform

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README.md

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+---
+language:
+- ja
+license: mit
+library_name: transformers
+tags:
+- math
+- chain-of-thought
+- reasoning
+- japanese
+- sft
+- llm-jp
+base_model: llm-jp/llm-jp-4-8b-v4-8b-decay2m-ipt_v3.1-instruct4
+pipeline_tag: text-generation
+---
+
+# acro-yamalex-llmjp-4-math-cot（モデル）
+
+日本語数学推論のためのChain-of-Thought (CoT) モデルです。
+[llm-jp-4-8b](https://huggingface.co/llm-jp/llm-jp-4-8b-v4-8b-decay2m-ipt_v3.1-instruct4)をベースに、306,366件の日本語CoTデータセットでSFT（教師あり微調整）を行いました。
+
+本モデルは[FT-LLM2026コンペティション](https://llm-jp.github.io/tuning-competition/)における我々のアプローチの中間成果物です。このCoTモデルをベースとして、さらにTIRデータでSFTを行い最終モデルを構築しています。
+
+## モデル概要
+
+| 項目 | 値 |
+|---|---|
+| ベースモデル | [llm-jp-4-8b](https://huggingface.co/llm-jp/llm-jp-4-8b-v4-8b-decay2m-ipt_v3.1-instruct4) (80億パラメータ) |
+| 学習手法 | SFT（教師あり微調整）/ フルパラメータ |
+| 学習データ | 日本語CoTデータセット（306,366件） |
+| フレームワーク | NeMo + Megatron-LM |
+| 精度 | bf16-mixed |
+
+## 学習設定
+
+[OpenMathReasoning](https://arxiv.org/abs/2504.16891)（NVIDIAのAIMO-2優勝手法）の学習手法に倣い、以下の設定でSFTを実施しました。
+
+| パラメータ | 値 |
+|---|---|
+| 学習率 | 2e-5 |
+| エポック数 | 2 |
+| バッチサイズ | 96 |
+| 最大系列長 | 4,096 |
+| オプティマイザ | AdamW (β₁=0.9, β₂=0.98) |
+| 重み減衰 | 0.1 |
+| ウォームアップ | 20 steps |
+| スケジューラ | CosineAnnealing |
+| 精度 | bf16-mixed |
+| PEFT | なし（フルパラメータ） |
+
+## 学習データ
+
+StackMathQAの問題に対してDeepSeek V3を用いて日本語CoT形式の解法を生成し、回答検証（文字列一致・数値比較・LLMジャッジ）を経て306,366件のデータを構築しました。
+
+詳細は[CoTデータセットのページ](https://huggingface.co/datasets/AcroYAMALEX/acro-yamalex-llmjp-4-math-cot)を参照してください。
+
+## 使い方
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "AcroYAMALEX/acro-yamalex-llmjp-4-math-cot"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="bfloat16", device_map="auto")
+
+prompt = """あなたは熟練した数学の専門家であり、注意深い問題解決者です。与えられた数学の問題に対して、ステップバイステップで思考し、最終的な答えを \\boxed{...} で示してください。
+
+### 指示:
+1から100までの自然数の和を求めてください。
+
+### 応答:
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## 入出力フォーマット
+
+### 入力
+
+システムプロンプトで数学の専門家としての役割を指定し、ユーザーメッセージとして数学の問題を与えます。
+
+### 出力
+
+`<think>`タグで囲まれた思考過程と、`\boxed{}`形式の最終回答を生成します。
+
+```
+<think>
+1からnまでの自然数の和の公式は n(n+1)/2 です。
+n = 100 を代入すると...
+</think>
+1から100までの自然数の和は、公式 n(n+1)/2 を用いて計算できます。
+
+$$\frac{100 \times 101}{2} = 5050$$
+
+$$\boxed{5050}$$
+```
+
+## 関連リソース
+
+| リソース | リンク |
+|---|---|
+| CoTデータセット | [AcroYAMALEX/acro-yamalex-llmjp-4-math-cot](https://huggingface.co/datasets/AcroYAMALEX/acro-yamalex-llmjp-4-math-cot) |
+| TIRデータセット | [AcroYAMALEX/acro-yamalex-llmjp-4-math-tir](https://huggingface.co/datasets/AcroYAMALEX/acro-yamalex-llmjp-4-math-tir) |
+| TIRモデル（最終モデル） | [AcroYAMALEX/acro-yamalex-llmjp-4-math-tir](https://huggingface.co/AcroYAMALEX/acro-yamalex-llmjp-4-math-tir) |
+| 論文 | NLP2026にて発表予定 |
+
+## 参考文献
+
+- Ivan Moshkov et al. "AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset." arXiv:2504.16891, 2025.
+- Ting Zhang et al. "StackMathQA: A Curated Collection of 2 Million Mathematical Questions and Answers Sourced from Stack Exchange." 2024.
+- DeepSeek-AI. "DeepSeek-V3 Technical Report." arXiv:2412.19437, 2024.
+
+## 著者
+
+佐々木峻・山本大輝・樋口慎・吉岡駿（アクロクエストテクノロジー株式会社）
+
+## ライセンス
+
+MIT License

config.json

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+{
+  "_name_or_path": "/groups/gch51701/Team020/cot/hf",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+  "rope_theta": 500000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.47.0",
+  "use_cache": true,
+  "vocab_size": 196608
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.47.0"
+}

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