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README.md

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----
-license: mit
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+---
+license: mit
+---
+library_name: transformers
+tags:
+- custom-code
+- qwen2
+- mla
+- gqa
+- attention-sinks
+license: apache-2.0
+language:
+- en
+- zh
+---
+
+# Qwen2.5-Coder-1.5B-Hybrid-v9
+
+## 🌟 Model Overview
+This is a custom-architected model based on `Qwen2.5-Coder-1.5B`. We introduced a novel **Asymmetric Hybrid Architecture (GQA + MLA)** with **Cross-Layer Shared Latent Gates** and **Attention Sinks**, enabling efficient feature communication and reduced KV-Cache memory footprint.
+
+## 🏗️ Architecture Innovations
+*(这里插入你用 picture.py 生成的架构图，可以把图片拖进 Hugging Face 网页版的编辑框里自动生成链接)*
+![Hybrid Architecture](填入你的图片链接)
+
+Unlike standard Qwen2 models, this `Hybrid-v9` backbone features:
+1. **Asymmetric Layers:** 
+   * **L0-L6:** Standard GQA (Grouped-Query Attention) for robust low-level feature extraction.
+   * **L7 (Shared Hub):** Generates a global latent vector $c_{kv}$ (Rank 320).
+   * **L8-L27:** Soft MLA (Multi-Head Latent Attention) with SVD-initialized low-rank projections.
+2. **Shared Latent Gate:** Deep layers can dynamically access the global latent vector from L7 via a learnable gating mechanism (`warmup_alpha`).
+3. **HybridCache & Attention Sinks:** Implements a sliding window (8192) alongside a 64-token attention sink to maintain generation stability at infinite sequence lengths.
+
+## 🚀 Quick Start
+
+**⚠️ IMPORTANT:** Because this model uses a custom architecture, you **MUST** pass `trust_remote_code=True` when loading it.
+
+### Prerequisites
+```bash
+pip install transformers torch