README.md

---
license: mit
---
library_name: transformers
tags:
- custom-code
- qwen2
- mla
- gqa
- attention-sinks
license: apache-2.0
language:
- en
- zh
---

# PyraCode-1.5B

## 🌟 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