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
2026-06-05 15:04:16 +08:00
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--- a/README.md
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+---
+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}
+}
+```
--- a/config.json
+++ b/config.json
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+{
+  "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,
+  "vocab_size": 196608
+}
--- a/generation_config.json
+++ b/generation_config.json
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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": [
+    2
+  ],
+  "pad_token_id": 2,
+  "transformers_version": "4.57.3"
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+    "model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
+    "model.norm.weight": "model-00004-of-00004.safetensors"
+  }
+}
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@@ -0,0 +1,45 @@
+{
+  "bos_token": {
+    "content": "<|bos|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<|cls|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eos|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<|mask|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|eos|>",
+  "sep_token": {
+    "content": "<|sep|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|unk|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
--- a/tokenizer.json
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93acf74eb5b234011e9eb42549c312551eb6726a16b9b0f8ed8920dfafe1298d
+size 12868527
--- a/tokenizer_config.json
+++ b/tokenizer_config.json