semvec/pypi-packages huggingface dataset:
test-util-project 0.1
A simple example package
License: MIT

# 测试上传

**版本:**
python3

一个根目录，包含所有代码。
一个 setup.py 文件，其中包含有关您的包的信息（名称、版本、依赖等）。
可选的 README.md 或 README.rst 文件，说明您的项目。
一个 LICENSE 文件，说明您的包的许可证。
一个包含 Python 代码的子目录。

my_package/
  setup.py
  README.md
  LICENSE
  my_package/
    __init__.py
    module1.py
    module2.py
moe-mamba 0.0.3
Paper - Pytorch
License: MIT
Requires Python: >=3.6,<4.0
Keywords: artificial intelligence,deep learning,optimizers,Prompt Engineering

[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)

# MoE Mamba
Implementation of MoE Mamba from the paper: "MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts" in Pytorch and Zeta. 

[PAPER LINK](https://arxiv.org/abs/2401.04081)


## Install

```bash
pip install moe-mamba
```

## Usage

### `MoEMambaBlock` 
```python
import torch 
from moe_mamba import MoEMambaBlock

x = torch.randn(1, 10, 512)
model = MoEMambaBlock(
    dim=512,
    depth=6,
    d_state=128,
    expand=4,
    num_experts=4,
)
out = model(x)
print(out)

```



## Code Quality 🧹

- `make style` to format the code
- `make check_code_quality` to check code quality (PEP8 basically)
- `black .`
- `ruff . --fix`


## Citation
```bibtex
@misc{pióro2024moemamba,
    title={MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts}, 
    author={Maciej Pióro and Kamil Ciebiera and Krystian Król and Jan Ludziejewski and Sebastian Jaszczur},
    year={2024},
    eprint={2401.04081},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

```


# License
MIT
openai-utilities 0.2.6
Wrapper on top of openai python SDK primarily to interface with openai and anyscale public endpoints.
License: MIT
Keywords: openai anyscale tiktoken chatgpt chatbot

This is a privately maintained wrapper library on top of openai python Driver/SDK. Currently openai python Driver/SDK can be used to access both OpenAI and Anyscale public endpoints. This wrapper provides abstraction to access the models hosted by both of the endpoints. It currently uses the openai.OpenAI().chat.completions to make synchronous calls. It automatically handles rate limit error, context window resizing and large message splitting to fit within message token limit. Look at https://github.com/soumitsalman/openai-utilities for more info.