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license: apache-2.0
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language:
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- en
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- multiligual
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pipeline_tag: text-generation
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tags:
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- on-device
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- privacy
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- offline
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- nlp
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- yongle-ai
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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library_name: transformers
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---
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# Yongle Nano 1.0
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<div align="center">
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<img src="
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<br>
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<b>Lightweight, private, offline intelligence for every device.</b>
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</div>
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## 🧠 Overview
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**Yongle Nano 1.0** is a compact, efficient language model designed for fast, private, on‑device use. It delivers reliable chat, writing assistance, summaries, and light coding support — even on older laptops.
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Nano 1.0 is part of the **Yongle Nano Series (1.x)**, your small‑model family optimized for speed, simplicity, and everyday tasks.
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---
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## ✨ Key Features
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* **Fully offline:** Zero cloud dependency.
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* **Fast on CPU:** Runs smoothly on standard CPU‑only devices.
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* **Low memory footprint:** Minimal RAM usage.
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* **Strong instruction following:** Understands and executes commands effectively.
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* **Good writing & summarization:** Capable of drafting and condensing text.
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* **Long‑context support:** Up to **32K tokens**.
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* **Multilingual:** Supports **29+ languages**.
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* **Energy‑efficient:** Privacy‑first architecture.
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---
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## 🎯 Intended Use
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* **Everyday chat:** Casual conversation and assistance.
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* **Writing & editing:** Drafting emails, essays, and correcting grammar.
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* **Summaries & explanations:** Condensing articles or explaining concepts.
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* **Light coding help:** Basic snippets and debugging.
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* **Education & learning:** Study buddy and tutor.
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* **Offline productivity:** Tools that work without Wi-Fi.
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* **Low‑spec hardware:** Ideal for laptops and desktops with limited resources.
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### 🚫 Not Intended For
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* Heavy reasoning or complex logic puzzles.
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* Multi‑document deep analysis.
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* Enterprise‑scale workloads.
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* Large‑context research tasks.
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---
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## ⚙️ Technical Details
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| Feature | Specification |
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| :--- | :--- |
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| **Internal Size** | 0.49B parameters |
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| **Architecture** | Transformer (RoPE, SwiGLU, RMSNorm, GQA) |
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| **Layers** | 24 |
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| **Attention Heads** | 14 (Q), 2 (KV) |
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| **Context Length** | 32,768 tokens |
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| **Generation Length** | Up to 8,192 tokens |
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| **Training** | Pretraining + Instruction tuning |
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---
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## 💻 Hardware Requirements
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**Minimum**
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* **RAM:** 4GB
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* **Processor:** CPU‑only (Works on most laptops from 2014–2024)
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**Recommended**
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* **RAM:** 8GB
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* **GPU:** Optional acceleration (2–4GB VRAM)
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---
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e(outputs[0], skip_special_tokens=True))
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