semvec/pypi-packages huggingface dataset:
ajoycon 1.0.1
Modern async Python driver for Nintendo Switch Joy-Con
License: MIT
Requires Python: >=3.13
Keywords: bluetooth, controller, joycon, nintendo, switch

# ajoycon

Modern async Python driver for Nintendo Switch Joy-Con controllers

Fork of [tokoroten-lab/joycon-python](https://github.com/tokoroten-lab/joycon-python)

## Install

### Using uv (recommended)

```shell
uv add ajoycon
```

### Using pip

```shell
pip install ajoycon
```

### Linux Setup

#### 1. Install system dependencies

```shell
sudo apt install libhidapi-dev libudev-dev
```

#### 2. Install hidapi with hidraw backend support

The default hidapi package doesn't support Bluetooth HID devices properly. Install from source:

**Using uv:**
```shell
uv remove hidapi
uv add hidapi --no-binary hidapi
```

**Using pip:**
```shell
pip uninstall hidapi
pip install hidapi --no-binary hidapi
```

#### 3. Setup udev rules

Use the provided installation script:

```shell
./install_udev.sh
```

Or manually:

```shell
sudo cp 50-nintendo-switch.rules /etc/udev/rules.d/
sudo udevadm control --reload-rules && sudo udevadm trigger
```

#### 4. Blacklist the kernel hid_nintendo driver

The kernel driver conflicts with hidapi access:

```shell
echo "blacklist hid_nintendo" | sudo tee /etc/modprobe.d/blacklist-nintendo.conf
sudo rmmod hid_nintendo  # Unload the module (or reboot)
```

#### 5. Connect your Joy-Cons

Pair and connect your Joy-Cons via Bluetooth, then reconnect them after completing the setup above.

## Usage

```python
import asyncio
from ajoycon import discover_joycons

async def main():
    joycons = discover_joycons()
    if not joycons:
        return

    async with joycons[0].connect() as joycon:
        status = joycon.status
        print(f"Battery: {status.battery.level.name}")
        print(f"Buttons: {status.buttons}")
        print(f"Sticks: {status.stick_l}, {status.stick_r}")
        print(f"IMU: {status.imu}")

asyncio.run(main())
```

## Requirements

- Python 3.13+
- hidapi (built with hidraw backend for Linux Bluetooth support)
- pyglm

### System Dependencies (Linux)

- libhidapi-dev
- libudev-dev

## License

MIT
ecotransformers 1.0.0
Towards Net-Zero AI: Reducing Latency and Energy Consumption with Eco-Transformers
License: MIT
Requires Python: >=3.8

EcoTransformers

Sustainable LLM Inference

EcoTransformers is a Python library designed for efficient and sustainable Large Language Model (LLM) inference.
It helps developers to reduce inference time, energy consumption, and CO₂ emissions — through lightweight, built-in optimizations.

Key Features:

   ◦ Optimized inference for Hugging Face transformer models

   ◦ Primat Technique — a unified optimization method that intelligently reduces redundant computations, skips negligible activations, and applies smart caching

   ◦ Accelerates inference and lowers the environmental cost of LLM experiments without compromising performance.

Installation:

Install directly from PyPI:

    pip install ecotransformers

Usage Examples:

1. Command-line Interface
python -m ecotransformers.main \
    --model "<your model name>" \
    --prompt "What are the benefits of sustainable AI?" \
    --reference "Sustainable AI reduces energy usage and CO₂ emissions."

2. Python API

from ecotransformers.main import transformer
results = transformer(
    model_name="<your model name>"
)
print(results)


PRIMAT(Pruning-Integrated Masked Activation for Transformers) Technique:

◦ The Primat Technique is the optimization engine behind EcoTransformers.
◦ PRIMAT uses adaptive pruning and activation masking for effective execution while maintaining model accuracy. 
◦ The Primat Technique is applied automatically — no manual configuration required.

License:
This project is licensed under the MIT License — see the LICENSE file for details.

Citation:

If you use EcoTransformers in your research, please cite:

@software{ecotransformers2025,
  author = {"Shriaarthy E","Sangeetha S"},
  title = {Towards Net-Zero AI: Reducing Latency and Energy Consumption with Eco-Transformers},
  year = {2025},
  url = {https://pypi.org/project/ecotransformers/},
  license = {MIT}
}