semvec/pypi-packages huggingface dataset:
cli-sliding-puzzle 0.1.4.post0
A CLI sliding puzzle game
License: MIT
Requires Python: >=3.7
Keywords: game, puzzle, cli

# CLI Sliding Puzzle Game

A command-line interface sliding puzzle game built with Python.

## Installation

To install the game, you need Python 3.7 or higher and pip. Then run:

pip install cli-sliding-puzzle

## Playing the Game

After installation, run the game by typing:

sliding-puzzle

Use arrow keys to move tiles. Press 'q' to quit.

## Development

### Setup

1. Clone the repository:
   git clone https://github.com/yourusername/cli-sliding-puzzle.git
   cd cli-sliding-puzzle

2. Create a virtual environment:
   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install development dependencies:
   pip install build twine setuptools_scm

### Building and Publishing

1. Ensure all changes are committed to git.

2. List existing tags to determine the next version number:
   ```bash
   git tag --list
   ```

3. Create a new git tag for the version:
   ```bash
   git tag -a vX.X.X -m "Release version X.X.X"
   git push origin vX.X.X
   ```

4. Clean up the dist directory:
   ```bash
   rm -rf dist/*
   ```

5. Build the package:
   ```bash
   python -m build
   ```

6. Test the package locally:
   ```bash
   # List the contents of the dist directory
   ls dist/
   
   # Install the latest version (replace X.X.X with the latest version number you see)
   pip install dist/cli_sliding_puzzle-X.X.X-py3-none-any.whl
   ```

7. Upload to TestPyPI:
   ```bash
   twine upload --repository testpypi dist/*
   ```

8. Test the TestPyPI upload:
   ```bash
   # Create a new virtual environment for testing
   python -m venv test_venv
   source test_venv/bin/activate  # On Windows, use `test_venv\Scripts\activate`

   # Install the package from TestPyPI
   pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple cli-sliding-puzzle

   # Test the installed package
   sliding-puzzle

   # Deactivate the test environment when done
   deactivate
   ```

9. If everything works, upload to PyPI:
   ```bash
   twine upload dist/*
   ```

## License

This project is licensed under the MIT License.
differential-transformer 0.0.3
differential-transformer - Pytorch
License: MIT
Requires Python: <4.0,>=3.10
Keywords: artificial intelligence, deep learning, optimizers, Prompt Engineering


# Differential Transformer 

An open source community implementation of the model from "DIFFERENTIAL TRANSFORMER" paper by Microsoft. [Paper Link](https://arxiv.org/abs/2410.05258). "Differential attention takes the difference between two softmax attention functions to eliminate attention noise. The idea is analogous to differential amplifiers [19] proposed in electrical engineering,where the difference between two signals is used as output, so that we can null out the common-mode noise of the input. In addition, the design of noise-canceling headphones is based on a similar idea. We can directly reuse FlashAttention [8] as described in Appendix A, which significantly improves model efficiency."



[![Join our Discord](https://img.shields.io/badge/Discord-Join%20our%20server-5865F2?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/agora-999382051935506503) [![Subscribe on YouTube](https://img.shields.io/badge/YouTube-Subscribe-red?style=for-the-badge&logo=youtube&logoColor=white)](https://www.youtube.com/@kyegomez3242) [![Connect on LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/kye-g-38759a207/) [![Follow on X.com](https://img.shields.io/badge/X.com-Follow-1DA1F2?style=for-the-badge&logo=x&logoColor=white)](https://x.com/kyegomezb)


## Install

```bash
$ pip3 install differential-transformers
```

## Usage Transformer

```python

import torch
from differential_transformer.main import DifferentialTransformer
from loguru import logger

# Example usage:
# Example dimensions
batch_size = 32
seq_len = 128
embedding_dim = 64
h = 8
λ = 0.1
λinit = 0.05

# Create random input tensor
x = torch.randint(0, 256, (1, 1024))

# Instantiate and run the multi-head attention
multi_head = DifferentialTransformer(heads=h, dim=embedding_dim, λinit=λinit)
output = multi_head(x, λ=λ)

logger.info(f"Output shape: {output.shape}")


```

# License
MIT


## Citation


```bibtex
@misc{ye2024differentialtransformer,
    title={Differential Transformer}, 
    author={Tianzhu Ye and Li Dong and Yuqing Xia and Yutao Sun and Yi Zhu and Gao Huang and Furu Wei},
    year={2024},
    eprint={2410.05258},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2410.05258}, 
}

```
jqgpt 0.1.1
jqgpt is a gpt powered tool that helps you write jq queries. It takes a human user query and a json file as input and outputs a jq query that answers the user query. It requires an OPENAI_API_KEY to work.
License: MIT
Requires Python: >=3.6

# jqgpt

jqgpt is a gpt powered tool that helps you write jq queries. It takes a human user query and a json file as input and outputs a jq query that answers the user query.

It accomplishes this by sending a very helpful jq prompt with tons of examples and a sample from the json file to the model.

## Examples

```
$ jqgpt 'Get the total number of clams of type dolphin' seaCreatures.json

jq 'map(select(.type == "dolphin").clams) | add' seaCreatures.json
```

```
$ cat seaCreatures.json | jqgpt 'Get the names of sea creatures'

jq -r '.[] | .name' seaCreatures.json
```

## Setup
You can install this using

```
pip install jqgpt
```

Requirements:
1. openai python package
2. OPENAI_API_KEY env var