semvec/pypi-packages huggingface dataset:
torchcast 0.1
A simple PyTorch library for time series analysis

## What is torchcast? <img align="right" src="docs/source/_static/torchcast_logo.png" width="100" height="100">

torchcast is a library for time series forecasting, classification, and regression in PyTorch. It focuses primarily on making it easy to fetch, preprocess, and iterate over time series datasets. **It is still under heavy construction.**

## Installation

torchcast includes C++ code that must be compiled to be used. To install torchcast, first make sure that you have g++ installed, along with Python, including its development headers. Then, run:

```
git clone https://github.com/TheoremEngine/torchcast.git
cd torchcast
python3 setup.py install
```
leanquant 0.1.1
The inference kernels for LeanQuant models.

# LeanQuant

## Overview

This package provides efficient inference kernels for running non-uniformly quantized LeanQuant models on CUDA-enabled GPUs. LeanQuant is a scalable and accurate quantization algorithm that compresses large language models by 4-8x while maintaining competitive performance.

## Installation

Ensure your GPU supports CUDA 11 or CUDA 12. You can check your CUDA version with the command `nvidia-smi | grep CUDA`.

To install:
```bash
# For CUDA 11.x
pip install leanquant[cuda11]

# For CUDA 12.x
pip install leanquant[cuda12]
```

## Models

Quantized LeanQuant models are available for download on our HuggingFace page: [huggingface.co/LeanQuant](https://huggingface.co/LeanQuant)

## Technical Details

LeanQuant introduces an innovative loss-error-aware grid approach to quantization that significantly outperforms traditional methods. Our technique:

- **Achieves superior compression ratios**: Reduces model size by 4-8x without sacrificing capability
- **Preserves model intelligence**: Maintains performance comparable to full-precision models across challenging benchmarks
- **Optimizes GPU execution**: Features custom CUDA kernels specifically designed for non-uniform quantization format

The algorithm strategically allocates quantization precision based on parameter sensitivity, ensuring computational resources are focused where they matter most.

For a comprehensive explanation of our methodology and benchmark results, please refer to our [research paper](https://arxiv.org/pdf/2407.10032).

## Citation

If you find LeanQuant useful in your research or applications, please consider citing our work:

```bibtex
@inproceedings{
    zhang2025leanquant,
    title={LeanQuant: Accurate and Scalable Large Language Model Quantization with Loss-error-aware Grid},
    author={Tianyi Zhang and Anshumali Shrivastava},
    booktitle={The Thirteenth International Conference on Learning Representations},
    year={2025},
    url={https://openreview.net/forum?id=ISqx8giekS}
}
```
deltachat-cursed 0.10.1
Delta Chat client for the command line
Requires Python: >=3.8
Keywords: deltachat, ncurses, tui

# Cursed Delta

> DEPRECATED: use arcanechat-tui instead

[![Latest Release](https://img.shields.io/pypi/v/deltachat-cursed.svg)](https://pypi.org/project/deltachat-cursed)
[![Downloads](https://pepy.tech/badge/deltachat-cursed)](https://pepy.tech/project/deltachat-cursed)
[![License](https://img.shields.io/pypi/l/deltachat-cursed.svg)](https://pypi.org/project/deltachat-cursed)
[![CI](https://github.com/adbenitez/deltachat-cursed/actions/workflows/python-ci.yml/badge.svg)](https://github.com/adbenitez/deltachat-cursed/actions/workflows/python-ci.yml)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

> a lightweight Delta Chat client for the command line

<p align="center">
  <img src="https://github.com/adbenitez/deltachat-cursed/blob/main/screenshots/e1.png?raw=true" alt="screenshot of Cursed Delta"/>
</p>

Cursed Delta is a ncurses Delta Chat client developed in Python with the urwid library.

#### Features

- [X] Create accounts
- [X] Tweak account configuration (ex. set display name and status)
- [X] Ability to send text messages :)
- [X] Read receipts ✓✓
- [X] Display quoted messages
- [ ] Account switcher
- [ ] Chat operations: delete, pin/unpin, mute/unmute, archive/unarchive, add/remove members, etc.
- [ ] Message operations: reply, delete, open attachment/links, see info, jump to quote
- [ ] Import/export keys and backups
- [ ] Ability to send files
- [ ] Notifications
- [ ] Support for contact verification and group invitations links
- [ ] Block/unblock contacts and see the list of blocked contacts
- [ ] See contact list
- [ ] Search messages and chats
- [ ] Open HTML messages
- [ ] View archived chats

## Installation

Install Cursed Delta with pip:

```
$ pip install -U deltachat-cursed
```

This program depends on the `deltachat-rpc-server` program to be installed in your system,
if you want to try to install it using pip run:

```
$ pip install -U deltachat-cursed[full]
```

After installation the command `curseddelta` (and `delta` alias) should be available.
For more tips and info check [the user guide](https://github.com/adbenitez/deltachat-cursed/blob/main/docs/user-guide.md)