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Model: AcroYAMALEX/acro-yamalex-llmjp-4-math-tir Source: Original Platform
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README.md
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---
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language:
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- ja
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license: mit
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library_name: transformers
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tags:
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- math
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- tool-integrated-reasoning
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- tir
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- code-execution
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- reasoning
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- japanese
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- sft
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- llm-jp
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base_model: llm-jp/llm-jp-4-8b-v4-8b-decay2m-ipt_v3.1-instruct4
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pipeline_tag: text-generation
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model-index:
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- name: acro-yamalex-llmjp-4-math-tir
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results:
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- task:
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type: text-generation
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name: 日本語数学推論
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dataset:
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name: FT-LLM2026 開発用評価データセット
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type: custom
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metrics:
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- type: accuracy
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value: 0.880
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name: 正解率（全体）
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- type: accuracy
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value: 1.000
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name: 正解率（中1）
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- type: accuracy
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value: 1.000
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name: 正解率（中2）
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- type: accuracy
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value: 0.938
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||||
name: 正解率（中3）
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- type: accuracy
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value: 0.867
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name: 正解率（数学I・A）
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- type: accuracy
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value: 0.731
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name: 正解率（数学II・B）
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- type: accuracy
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value: 0.800
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name: 正解率（数学III・C）
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---
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# acro-yamalex-llmjp-4-math-tir（モデル）
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日本語数学推論のためのTool-Integrated Reasoning (TIR) モデルです。
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CoTモデル（llm-jp-4-8bをCoTデータでSFTしたモデル）をベースに、134,834件のTIRデータセットでさらにSFTを行った**最終モデル**です。
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自然言語による推論とPythonコード実行を組み合わせて数学問題を解くことができます。
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開発用評価データセット（100問）において**正解率88.0%**を達成しました。
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本モデルは[FT-LLM2026コンペティション](https://llm-jp.github.io/tuning-competition/)における我々の提出モデルです。
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## モデル概要
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| 項目 | 値 |
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|---|---|
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| 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) |
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| 学習手法 | 2段階SFT（CoT SFT → TIR SFT）/ フルパラメータ |
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| 学習データ | CoTデータ（306,366件）→ TIRデータ（134,834件） |
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| フレームワーク | NeMo + Megatron-LM |
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| 精度 | bf16-mixed |
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## 学習パイプライン
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[OpenMathReasoning](https://arxiv.org/abs/2504.16891)（NVIDIAのAIMO-2優勝手法）に倣い、2段階のSFTを実施しました。
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```
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llm-jp-4-8b (ベースモデル)
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↓ CoT SFT（306,366件）
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CoTモデル
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↓ TIR SFT（134,834件）
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TIRモデル（本モデル）★
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```
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## 学習設定
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| パラメータ | CoT SFT（第1段階） | TIR SFT（第2段階） |
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|---|---|---|
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| ベースモデル | llm-jp-4-8b | CoTモデル |
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| 学習率 | 2e-5 | 2e-5 |
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| エポック数 | 2 | 2 |
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| バッチサイズ | 96 | 64 |
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| 最大系列長 | 4,096 | 4,096 |
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| オプティマイザ | AdamW (β₁=0.9, β₂=0.98) | AdamW (β₁=0.9, β₂=0.98) |
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| 重み減衰 | 0.1 | 0.1 |
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| ウォームアップ | 20 steps | 20 steps |
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| スケジューラ | CosineAnnealing | CosineAnnealing |
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| 精度 | bf16-mixed | bf16-mixed |
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| PEFT | なし（フルパラメータ） | なし（フルパラメータ） |
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## 評価結果
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開発用評価データセット（100問）における正解率です。推論時には1問あたり15個の回答候補を生成し、多数決（majority voting）により最終回答を決定しています。
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| カテゴリ | 正解率 |
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|---|---|
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| 中1 | 1.000 |
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| 中2 | 1.000 |
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| 中3 | 0.938 |
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| 数学I・A | 0.867 |
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| 数学II・B | 0.731 |
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| 数学III・C | 0.800 |
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| **全体** | **0.880** |
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### 推論設定
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| パラメータ | 値 |
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|---|---|
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| サンプル数（多数決用） | 15 |
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| 最大コード実行回数 | 5回 |
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| 最大生成トークン数 | 4,096 |
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## 使い方
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### 基本的な使い方
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "AcroYAMALEX/acro-yamalex-llmjp-4-math-tir"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="bfloat16", device_map="auto")
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prompt = """あなたは「自然言語の推論」と「Pythonコードの実行」を組み合わせて数学問題を解くアシスタントです。
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### 指示:
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1から100までの自然数の和を求めてください。
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### 応答:
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.7)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### TIR推論パイプライン（コード実行付き）
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本モデルの性能を最大限に引き出すには、生成されたPythonコードブロックを検出・実行し、結果をモデルにフィードバックするTIR推論パイプラインが必要です。
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1. モデルが応答を生成（思考過程 + Pythonコード）
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2. `` ```python `` ブロックを検出し、サンドボックスで実行
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3. 実行結果を `` ```output `` ブロックとしてモデルにフィードバック
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4. 最終回答（`\boxed{}`形式）が得られるまで繰り返し（最大5回）
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5. 複数サンプル（15個推奨）を生成し、多数決で最終回答を決定
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## 入出力フォーマット
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### 入力
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システムプロンプトでTIR推論の役割を指定し、ユーザーメッセージとして数学の問題を与えます。
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### 出力
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`<think>`タグで囲まれた思考過程、Pythonコードブロック、コード実行結果を含むマルチターン対話を生成し、最終的に`\boxed{}`形式で回答を出力します。
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```
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<think>
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Python を使ってシミュレーションしよう。
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</think>
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```python
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total = sum(range(1, 101))
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print(total)
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```
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```output
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5050
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```
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<think>
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計算結果は5050です。
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</think>
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1から100までの自然数の和は:
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$$\boxed{5050}$$
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```
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## 関連リソース
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| リソース | リンク |
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|---|---|
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| 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) |
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| 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) |
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| CoTモデル（中間モデル） | [AcroYAMALEX/acro-yamalex-llmjp-4-math-cot](https://huggingface.co/AcroYAMALEX/acro-yamalex-llmjp-4-math-cot) |
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| 論文 | NLP2026にて発表予定 |
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## 参考文献
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- Ivan Moshkov et al. "AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset." arXiv:2504.16891, 2025.
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- Ting Zhang et al. "StackMathQA: A Curated Collection of 2 Million Mathematical Questions and Answers Sourced from Stack Exchange." 2024.
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- Keiran Paster et al. "OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text." arXiv:2310.06786, 2024.
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- DeepSeek-AI. "DeepSeek-V3 Technical Report." arXiv:2412.19437, 2024.
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## 著者
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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）
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## ライセンス
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MIT License
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config.json
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||||
51
special_tokens_map.json
Normal file
51
special_tokens_map.json
Normal file
@@ -0,0 +1,51 @@
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||||
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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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oid sha256:93acf74eb5b234011e9eb42549c312551eb6726a16b9b0f8ed8920dfafe1298d
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size 12868527
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2837
tokenizer_config.json
Normal file
2837
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user