Create README.md

README.md

@@ -0,0 +1,68 @@
+# 小參數長鏈思考模型（Chain-of-Thought for Traditional Chinese）  
+Finetuned LLaMA 3.2 3B on Chain-of-Thought Reasoning in Traditional Chinese
+
+## 模型簡介 | Model Overview
+
+這是一個專為繁體中文社群設計的開源長鏈思考（Chain-of-Thought, CoT）微調模型，基於 Meta LLaMA 3.2 3B 架構進行微調，聚焦於增強模型在繁體中文語境下的推理能力與多步邏輯表達。
+
+This is an open-source Chain-of-Thought (CoT) finetuned model for the Traditional Chinese community, built upon Meta's LLaMA 3.2 3B architecture. It enhances multi-step reasoning and logical articulation within a Traditional Chinese context.
+
+## 訓練動機 | Training Motivation
+
+作為一名人工智慧愛好者，我發現目前針對繁體中文語境的長鏈思考模型仍然稀缺，許多開源模型偏向英文或簡體中文。因此，我著手打造此模型，期望為繁體中文用戶提供更合適的邏輯推理基礎模型，並推廣 CoT 技術在華語世界的應用與理解。
+
+As an AI enthusiast, I noticed the scarcity of open-source CoT models tailored for Traditional Chinese. Most models are either English-based or optimized for Simplified Chinese. This project aims to fill that gap, offering a dedicated reasoning-friendly model for Traditional Chinese users, and promoting CoT applications in the broader Chinese-speaking world.
+
+## 特性簡述 | Key Features
+
+- 語言支援：專為繁體中文設計，保留文化語感  
+- 推理能力：優化多步邏輯思考與問題拆解  
+- 開源導向：歡迎社群參與微調與改進  
+- 小參數模型：3B 規模，適合在中等資源設備上運行
+
+- Language Support: Tuned for Traditional Chinese with cultural nuance  
+- Reasoning-Ready: Enhanced for multi-step problem-solving  
+- Open-Source Friendly: Community contributions are welcome  
+- Lightweight: 3B size, ideal for moderate hardware environments
+
+## 訓練細節 | Training Details
+
+- Base Model：Meta LLaMA 3.2 3B  
+- 微調任務：Chain-of-Thought prompting in Traditional Chinese  
+- 資料集來源：自建與繁體化處理的開源數據（涵蓋數學、邏輯推理、日常問答等）  
+- 訓練策略：使用 LoRA 精簡微調技術（Low-Rank Adaptation），提升上下文理解與推理連貫性  
+- 硬體資源：單張 NVIDIA RTX 4060，進行約 16 小時微調  
+- 訓練框架：基於 Hugging Face Transformers + PEFT + bitsandbytes 訓練
+
+### Training Details (English)
+
+- Base Model: Meta LLaMA 3.2 3B  
+- Task: Chain-of-Thought prompting in Traditional Chinese  
+- Dataset: Custom-built and adapted Traditional Chinese datasets (math, logical reasoning, daily QA)  
+- Tuning Strategy: Lightweight LoRA finetuning to boost context handling and step-by-step reasoning  
+- Hardware: Trained on a single NVIDIA RTX 4060 GPU for approximately 16 hours  
+- Framework: Powered by Hugging Face Transformers, PEFT, and bitsandbytes
+
+## 使用建議 | Usage Tips
+
+此模型適用於以下應用場景：  
+- 推理任務與數學解題  
+- 教學與邏輯問答  
+- 多步驟任務規劃
+
+適合與 CoT 提示語搭配，例如：  
+「請一步一步解釋你的推理過程。」
+
+Recommended for tasks such as:  
+- Logical reasoning and math problems  
+- Educational QA  
+- Step-by-step task planning
+
+Pair well with CoT-style prompts like:  
+"Please explain your reasoning step by step."
+
+## 歡迎貢獻 | Contribute
+
+此模型開放給社群一同優化與實驗，若你也關心繁體中文在 AI 領域的表現，歡迎 fork、finetune 或提交 PR。
+
+This model is open to community collaboration. If you care about advancing Traditional Chinese capabilities in AI, feel free to fork, finetune, or open a PR!