semvec/pypi-packages huggingface dataset:
nonebot-plugin-diffsinger 0.1.13
用DiffSinger让bot唱歌
License: MIT
Requires Python: >=3.8

这个需要配合这个项目使用（[DiffSingerMiniEngine](https://github.com/zhzhongshi/DiffSingerMiniEngine)

怎么搭？
我只在windows上跑的，欢迎PR（）

```
python -m venv ds
ds\Scripts\activate
git clone https://github.com/zhzhongshi/DiffSingerMiniEngine
cd DiffSingerMiniEngine
# 从release里把模型下下来解压丢进去（
pip install -r requirememts.txt -U
python server.py
```

然后搭建nb，从nb里把插件跑起来
我默认你会用nb-cli

```
nb plugin install nonebot-plugin-diffsinger
```

插件会自动添加到你pyproject.toml里面

你要是打算用pip安装的话

```
pip install nonebot-plugin-diffsinger
```

别忘了把"nonebot-plugin-diffsinger"加到pyproject.toml里面的"plugins"里面

配置

```
DS_URL="http://127.0.0.1:9266/" #后端的地址
DS_SPEEDUP=200 #后端的推理速度，建议10-255
```

测试指令

```
/diffsinger 120~[
[60,2,"AP"],
[57,2,"ba"],
[59,2,"ni"],
[60,2,"peng"],
[64,2,"zai"],
[62,2,"shou"],
[60,2,"+"],
[62,8,"shang"],
[60,4,"AP"],
[57,2,"he"],
[59,2,"qi"],
[60,12,"le"],
[59,4,"shou"],
[55,16,"zhang"],
[60,4,"AP"]
]|120~[
[60,4,"dao"],
[62,4,"lai"],
[64,4,"mi"],
[65,4,"fa"],
[67,4,"sao"],
[69,4,"la"],
[71,4,"xi"]
]|90~[
[60,4,"dao"],
[62,4,"lai"],
[64,4,"mi"],
[65,4,"fa"],
[67,4,"sao"],
[69,4,"la"],
[71,4,"xi"]
]
```
exxa 0.6.4
Exa - Pytorch
License: MIT
Requires Python: <4.0,>=3.10
Keywords: artificial intelligence, deep learning, optimizers, Prompt Engineering

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

# Exa
Boost your GPU's LLM performance by 300% on everyday GPU hardware, as validated by renowned developers, in just 5 minutes of setup and with no additional hardware costs.

-----

## Principles
- Radical Simplicity (Utilizing super-powerful LLMs with as minimal lines of code as possible)
- Ultra-Optimizated Peformance (High Performance code that extract all the power from these LLMs)
- Fludity & Shapelessness (Plug in and play and re-architecture as you please)

---

## 📦 Install 📦
```bash
$ pip3 install exxa
```
-----


## Usage






## 🎉 Features 🎉

- **World-Class Quantization**: Get the most out of your models with top-tier performance and preserved accuracy! 🏋️‍♂️
  
- **Automated PEFT**: Simplify your workflow! Let our toolkit handle the optimizations. 🛠️

- **LoRA Configuration**: Dive into the potential of flexible LoRA configurations, a game-changer for performance! 🌌

- **Seamless Integration**: Designed to work seamlessly with popular models like LLAMA, Falcon, and more! 🤖

----

## 💌 Feedback & Contributions 💌

We're excited about the journey ahead and would love to have you with us! For feedback, suggestions, or contributions, feel free to open an issue or a pull request. Let's shape the future of fine-tuning together! 🌱

[Check out our project board for our current backlog and features we're implementing](https://github.com/users/kyegomez/projects/8/views/2)


# License
MIT

# Todo

- Setup utils logger classes for metric logging with useful metadata such as token inference per second, latency, memory consumption
- Add cuda c++ extensions for radically optimized classes for high performance quantization + inference on the edge
mamba-former 0.0.3
Paper - Pytorch
License: MIT
Requires Python: <4.0,>=3.9
Keywords: artificial intelligence, deep learning, optimizers, Prompt Engineering

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

# MambaFormer
Implementation of MambaFormer in Pytorch ++ Zeta from the paper: "Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks"

## install
`pip3 install mamba-former`

## usage
```python
import torch
from mamba_former.main import MambaFormer

# Forward pass example
x = torch.randint(1, 1000, (1, 100))  # Token
# Tokens are integers representing input data

# Model
model = MambaFormer(
    dim=512,  # Dimension of the model
    num_tokens=1000,  # Number of unique tokens in the input data
    depth=6,  # Number of transformer layers
    d_state=512,  # Dimension of the transformer state
    d_conv=128,  # Dimension of the convolutional layer
    heads=8,  # Number of attention heads
    dim_head=64,  # Dimension of each attention head
    return_tokens=True,  # Whether to return the tokens in the output
)

# Forward pass
out = model(x)  # Perform a forward pass through the model

# If training
# out = model(x, return_loss=True)  # Perform a forward pass and calculate the loss

# Print the output
print(out)  # Print the output tensor
print(out.shape)  # Print the shape of the output tensor

```


# License
MIT
coveragespace 6.1
A place to track your code coverage metrics.
License: MIT
Requires Python: <4.0,>=3.8
Keywords: coverage, testing, command-line

# Overview

The official command-line client for [The Coverage Space](http://coverage.space).

[![Build Status](https://img.shields.io/github/actions/workflow/status/jacebrowning/coverage-space-cli/main.yml?branch=main&label=build)](https://github.com/jacebrowning/coverage-space-cli/actions)
[![Coverage Status](https://img.shields.io/coveralls/jacebrowning/coverage-space-cli/main.svg)](https://coveralls.io/r/jacebrowning/coverage-space-cli)
[![Scrutinizer Code Quality](https://img.shields.io/scrutinizer/g/jacebrowning/coverage-space-cli.svg)](https://scrutinizer-ci.com/g/jacebrowning/coverage-space-cli/?branch=main)
[![PyPI Version](https://img.shields.io/pypi/v/coveragespace.svg)](https://pypi.org/project/coveragespace)
[![PyPI License](https://img.shields.io/pypi/l/coveragespace.svg)](https://pypi.org/project/coveragespace)

# Setup

## Requirements

- Python 3.7+

## Installation

Install this tool globally with [pipx](https://pipxproject.github.io/pipx/) (or pip):

```text
$ pipx install coveragespace
```
or add it to your [Poetry](https://python-poetry.org/docs/) project:

```text
$ poetry add coveragespace
```

# Usage

To update the value for a test coverage metric:

```text
$ coveragespace update <metric>
```

where `<metric>` is one of:

- **unit**
- **integration**
- **overall**

For example, after running unit tests with code coverage enabled:

```text
$ coveragespace update unit
```

which will attempt to extract the current coverage data from your project directory and compare that with the last known value. The coverage value can also be manually specified:

```text
$ coveragespace update <metric> <value>
```