Metadata-Version: 2.1
Name: nixoptimiz
Version: 0.1.1
Summary: Nix Optimizer in PyTorch
Author: Project VsingerXiaoice Consensus
Author-email: alan_sudo@yeah.net
License: The MIT License (MIT)
        
        Copyright © 2025 Project VsingerXiaoice Consensus
        
        Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Requires: torch
Description-Content-Type: text/markdown
License-File: LICENSE

# Nix

Nix is an simple optimizer for optimize complex models. Nix是一个用于优化复杂模型的简单优化器。

## Usage 使用

Here is example code 以下是示例代码：

```python
from nixoptimiz import NixOptimizer
import torch
from torch import nn

# Example Model
model = nn.Sequential(
    nn.Linear(16, 512),
    nn.ReLU(),
    nn.Linear(512, 16),
    nn.Sigmoid()
).cuda()

# Example Data
inputs = torch.randn(64, 16).cuda()
outputs = torch.rand(64, 16).cuda()

optim = NixOptimizer(model.parameters(), lr=0.01, muon_mode="kernel")
criterion = nn.BCELoss()

for idx in range(0, 5001):
    loss = criterion(model(inputs), outputs)
    optim.zero_grad()
    loss.backward()
    optim.step()
    if idx % 100 == 0:
        print(f"Train step: {idx}, Loss: {float(loss.cpu())}")
```
