commit bf80aa0fcf87b2d0356f9f14c6b836a95dba060b Author: ModelHub XC Date: Wed Apr 22 03:58:26 2026 +0800 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 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 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100644 index 0000000..be2b47d --- /dev/null +++ b/README.md @@ -0,0 +1,122 @@ +--- +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 + +``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。 + +``` + +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... + +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 diff --git a/config.json b/config.json new file mode 100644 index 0000000..932a153 --- /dev/null +++ b/config.json @@ -0,0 +1,30 @@ +{ + "_name_or_path": "/groups/gch51701/Team020/cot/hf", + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 1, + "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, + "pretraining_tp": 1, + "rms_norm_eps": 1e-06, + "rope_scaling": null, + "rope_theta": 500000, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.47.0", + "use_cache": true, + "vocab_size": 196608 +} diff 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