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CortexNet: unified neural network architecture beyond Transformers

Project description

CortexNet

CI PyPI version License

Language / 语言

  • 中文文档入口: docs/README.md(Chinese + English full docs index)
  • English quick docs: docs/en/QUICKSTART_AND_USAGE.md
  • Chinese quick docs: docs/zh-CN/QUICKSTART_AND_USAGE.md

CortexNet 是一个面向语言建模与推理场景的神经网络架构实现,核心思路是将多尺度状态空间建模、选择性稀疏注意力、记忆系统、条件路由与可选高级推理模块组合到同一框架中。

CortexNet is a unified neural architecture implementation for language modeling and reasoning, combining multi-scale SSM, selective sparse attention, memory, conditional routing, and optional advanced reasoning modules in one framework.

本仓库已经完成以下整理:

  • 统一对外主模型命名为 CortexNet(不再以 *V3 作为主入口)
  • 代码按 pip 可发布标准重构到 cortexnet/ 包目录
  • 基准/聊天/评测脚本统一归档到 scripts/
  • 清理无用导入、冗余变量和缓存文件
  • 补充架构文档和模块映射文档,方便开源协作

Installation

pip install -e .

或构建并安装 wheel:

python -m pip install build
python -m build
pip install dist/*.whl

CLI

python -m cortexnet --version
python -m cortexnet --smoke-test

Quick Start

import torch
from cortexnet import CortexNet, CortexNetConfig

config = CortexNetConfig(
    vocab_size=32000,
    hidden_size=512,
    num_layers=4,
    num_heads=8,
    max_seq_len=2048,
)

model = CortexNet(config).eval()
input_ids = torch.randint(0, config.vocab_size, (1, 16))

with torch.no_grad():
    out = model(input_ids)
print(out["logits"].shape)

Package Layout

cortexnet/
  adapter/      # 开源模型识别、权重映射、架构适配、推理适配、校准
  ops/          # 设备与算子抽象(CPU/CUDA/MPS/NPU/MLU)
  model.py      # 主模型与 from_pretrained
  blocks.py     # 核心 block 组合
  ...
scripts/
  benchmarks/   # 性能与量化基准
  chat/         # 聊天脚本
  eval/         # 能力评测脚本
  dev/          # 一键流程与测试运行器
tests/          # 回归与单元测试

Test

python -m pytest -q

Docs

  • 文档中心(中英文完整文档):docs/README.md
  • 架构说明:ARCHITECTURE.md
  • 模块职责图:MODULE_MAP.md
  • 变更记录:CHANGELOG.md
  • 贡献指南:CONTRIBUTING.md
  • 支持与问题分流:SUPPORT.md
  • 安全策略:SECURITY.md
  • 示例代码:examples/README.md
  • 发布基准报告:docs/reports/README.md
  • Hugging Face 发布:docs/en/HF_PUBLISHING.md / docs/zh-CN/HF_PUBLISHING.md

Compatibility Notes

  • 对外推荐只使用 CortexNet
  • 历史命名(如 CortexNetV2 / CortexNetV3)仍保留为兼容别名,避免旧代码立即失效。

Development Commands

make install-dev
make lint
make test-all
make check

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