commit 926919bb0c3ce28b266c5936e0c15da8997e4872 Author: ModelHub XC Date: Fri Jun 5 15:04:16 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: azuki-digital/llm-jp-4-math-lion Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text 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100644 index 0000000..451be01 --- /dev/null +++ b/README.md @@ -0,0 +1,226 @@ +--- +language: +- ja +license: apache-2.0 +tags: +- math +- japanese +- fine-tuning +- sft +- lora +- chain-of-thought +- self-consistency +- llm-jp +- ft-competition-2026 +base_model: +- llm-jp/llm-jp-4-8b-base +datasets: +- azuki-digital/ft-llm-2026-synthetic-ja-math-qwen-235b-v1 +- azuki-digital/ft-llm-2026-synthetic-ja-math-qwen-235b-v2 +pipeline_tag: text-generation +model-index: +- name: llm-jp-4-math-lion + results: + - task: + type: math-reasoning + name: Japanese Math Benchmark (Dev) + metrics: + - type: accuracy + value: 95 + name: Accuracy (Dev 100Q) +--- + +# llm-jp-4-math-lion + +**ファインチューニングコンペ LLM 2026 — 数学タスク（オープン枠）提出モデル** + +日本の中学・高校レベルの数学問題に対する推論精度を最大化するために、2段階のSFT（Supervised Fine-Tuning）と推論時のSelf-Consistencyを組み合わせたモデルです。 + +## モデル概要 + +| 項目 | 詳細 | +|------|------| +| ベースモデル | LLM-JP 4 継続事前学習済みモデル（`llm-jp/llm-jp-4-8b-base`）| +| 学習手法 | 2段階 SFT（Stage 1: Full Fine-Tuning → Stage 2: LoRA） | +| 学習データ | 合成データ 約160万件（自作 + LLM蒸留） | +| 推論手法 | vLLM + `\boxed{}` 形式出力（Self-Consistency対応） | +| 開発ベンチマーク精度 | 95%（運営配布100問） | +| コンペ | ファインチューニングコンペ LLM 2026 数学タスク・オープン枠 | + + +## ベースモデルの選定 + +運営から提供された3つのベースモデル候補を開発用ベンチマーク100問で評価し、以下の結果を得ました。 + +| モデル | 精度 | +|--------|------| +| 事前学習のみ | 7% | +| 継続事前学習済み | 48% | +| インストラクションチューニング済み | 44% | + +継続事前学習とSFTを組み合わせる方針だったため、追加の継続事前学習が行いやすく精度も最も高かった「継続事前学習済みモデル」を選定しました。 + +## 学習データセット + +2種類のデータセットを作成し、段階的にモデルへ学習させています。 + +### データセット1: ベンチマーク指向型データ（約60万件） + +評価ベンチマークの出題形式・分野を意識して作成したデータセットです。 + +- **問題生成**: LLMは不使用。日本語の設問テンプレートを手動で作成し、ランダムに組み合わせるロジックで生成。計算部分はSymPyを使用して式と回答の整合性を保証。 +- **カバー範囲**: 中学・高校数学の評価対象カテゴリを網羅。約3万件のユニークな問題・回答ペアを作成。 +- **CoT付与**: 各問題に対して Qwen/Qwen3-235B-A22B-Instruct-2507 で20個のCoTを生成（Temperature / Top-p制御で多様性を確保）。生成されたCoTと正解を突き合わせ、正確なもののみを採用。 +- **最終データ量**: 約60万件（3万問 × 正確なCoT） + +### データセット2: LLM完全生成型データ（約100万件） + +問題・CoTの両方をLLMで生成した蒸留データセットです。NVIDIAの数学タスク精度向上に関する研究を参考にしています。 + +- **問題生成**: Qwen/Qwen3-235B-A22B-Instruct-2507 を使用。日本の中学・高校の数学カリキュラムに準拠した問題を全カテゴリで約5万件生成。ベンチマークとは独立した出題で汎化性能を意識。 +- **回答・CoT生成**: Qwen/Qwen3-235B-A22B-Instruct-2507 を使用。各問題に対して20回の試行を行い、マジョリティ・ボーティング（多数決）で80%以上の一致率が得られたもののみを採用。 +- **最終データ量**: 約100万件（5万問 × 正確なCoT） + +## 学習手法 + +### Stage 1: Full Fine-Tuning + +汎用的な数学推論能力の獲得を目的とし、LLM完全生成型データセット（約100万件：azuki-digital/ft-llm-2026-synthetic-ja-math-qwen-235b-v2）でフルパラメータのファインチューニングを実施。 + +| パラメータ | 値 | +|-----------|-----| +| 手法 | Full Fine-Tuning | +| 学習率 | 2e-5 | +| データ | LLM完全生成型データ（~100万件） | + +### Stage 2: LoRA Fine-Tuning + +ベンチマークの出題形式への適応を目的とし、ベンチマーク指向型データセット（約60万件：azuki-digital/ft-llm-2026-synthetic-ja-math-qwen-235b-v1）でLoRAによる追加学習を実施。 + +| パラメータ | 値 | +|-----------|-----| +| 手法 | LoRA | +| ランク (r) | 64 | +| Alpha (α) | 128 | +| ドロップアウト | 0.03 | +| 学習率 | 1e-4 | +| データ | ベンチマーク指向型データ（~60万件） | + +## 使い方 + +### プロンプトテンプレート + +本モデルは以下のプロンプトテンプレートで学習・推論を行っています。出力は必ず `\boxed{}` で最終回答を囲む形式になります。 + +``` +次の数学の問題を解いてください。 + +【厳守】 +- 数式はLaTeXで書く。 +- 最後の答えは必ず \boxed{} で1回だけ囲む。 + +問題: +{problem} + +解答: +``` + +### 推論コード（vLLM） + +```python +from vllm import LLM, SamplingParams + +PROMPT_TEMPLATE = """\ +次の数学の問題を解いてください。 + +【厳守】 +- 数式はLaTeXで書く。 +- 最後の答えは必ず \\boxed{{}} で1回だけ囲む。 + +問題: +{problem} + +解答: +""" + +model_path = "azuki-digital/llm-jp-4-math-lion" +llm = LLM(model=model_path, trust_remote_code=True) + +sampling_params = SamplingParams( + temperature=0.0, + top_p=1.0, + max_tokens=4096, +) + +problem = "2次方程式 x^2 - 5x + 6 = 0 を解きなさい。" +prompt = PROMPT_TEMPLATE.format(problem=problem) + +outputs = llm.generate([prompt], sampling_params=sampling_params) +print(outputs[0].outputs[0].text) +``` + +### 回答の抽出 + +モデルの出力から最終回答を取得するには、`\boxed{}` の中身を抽出してください。 + +```python +import re + +def extract_answer(text: str) -> str: + """text から最後の \\boxed{...} の中身を抽出する""" + key = r"\boxed{" + start = text.rfind(key) + if start == -1: + return "" + i = start + len(key) + depth = 1 + out_chars = [] + while i < len(text): + ch = text[i] + if ch == "{": + depth += 1 + out_chars.append(ch) + elif ch == "}": + depth -= 1 + if depth == 0: + return "".join(out_chars).strip() + out_chars.append(ch) + else: + out_chars.append(ch) + i += 1 + return "" +``` + +### 推論時のTips + +- **Self-Consistency（オプション）**: 精度をさらに向上させたい場合、同一問題に対して複数回（例: 20回）推論を行い、[Math-Verify](https://github.com/huggingface/Math-Verify)で回答を比較した上でマジョリティ・ボーティングを行う手法が有効です。学習に使用しなかった外部ベンチマークでは、この手法により精度が顕著に向上する傾向が確認されました。 +- **リトライ**: `\boxed{}` が出力に含まれない場合は、同じ問題で再度推論を行うことで回答を得られる場合があります。 + +## 評価結果 + +| ベンチマーク | 精度 | 備考 | +|-------------|------|------| +| 開発用ベンチマーク（100問） | **95%** | 運営配布の評価データ | + +参考として、Qwen/Qwen3-30B-A3B-Instruct-2507 の同ベンチマークに対する精度は84%であり、本モデルはこれを上回る精度を達成しています。 + +## 制限事項 + +- 日本の中学・高校レベルの数学問題に特化して学習しているため、それ以外の領域での性能は保証されません。 +- 合成データ（Qwen3による蒸留データ）を中心に学習しているため、学習データの分布から外れるパターンの問題では精度が低下する可能性があります。 +- 継続事前学習は当初計画していたものの、リソース・スケジュールの制約により未実施です。 + +## ライセンス + +Apache License 2.0 + +## 引用 + +```bibtex +@misc{llm-jp-4-math-lion, + title={llm-jp-4-math-lion: Two-Stage SFT with Self-Consistency for Japanese Math Reasoning}, + author={azuki-digital}, + year={2026}, + url={https://huggingface.co/azuki-digital/llm-jp-4-math-lion} +} +``` \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..519eb87 --- /dev/null +++ b/config.json @@ -0,0 +1,30 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 4096, + "mlp_bias": false, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "pretraining_tp": 1, + "rms_norm_eps": 1e-06, + "rope_scaling": null, + "rope_theta": 500000, + "tie_word_embeddings": false, + "transformers_version": "4.57.3", + "use_cache": false, + 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