semvec/pypi-packages huggingface dataset:
hyperpod-nemo-adapter 100.0.0
sdfsdf sdafaf safafa sfsdf



# Installation


To install requirements: `python -m pip install requirements.txt`

To save requirements: `python -m pip list --format=freeze --exclude-editable -f https://download.pytorch.org/whl/torch_stable.html > requirements.txt`

* Note we use Python 3.9.4 for our experiments

# Running the code 

For remaining experiments: 

Navigate to the corresponding directory, then execute: `python run.py -m` with the corresponding `config.yaml` file (which stores experiment configs).

# License

Consult License.md
cast-planet-api 0.1.3
A package for searching and retrieving imagery via the Planet REST API
Requires Python: >=3.9

# Planet API Wrapper
A little wrapper package for working with the [Planet APIs](https://developers.planet.com/docs/apis/)

Currently supported apis:
- [Data](https://developers.planet.com/docs/apis/data/)
- [Orders](https://developers.planet.com/apis/orders/)
manta-fpga 1.1.0
A configurable and approachable tool for FPGA debugging and rapid prototyping
Requires Python: >=3.9

![](https://raw.githubusercontent.com/fischermoseley/manta/refs/heads/main/doc/assets/logo.png)

## Manta: A Configurable and Approachable Tool for FPGA Debugging and Rapid Prototyping
![run_tests](https://github.com/fischermoseley/manta/actions/workflows/run_tests.yml/badge.svg)
![build_docs](https://github.com/fischermoseley/manta/actions/workflows/build_docs.yml/badge.svg)
[![codecov](https://codecov.io/gh/fischermoseley/manta/graph/badge.svg?token=1GGHCICK3Q)](https://codecov.io/gh/fischermoseley/manta)
[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

Manta is a tool for getting information into and out of FPGAs over an interface like UART or Ethernet. It's primarily intended for debugging, but it's robust enough to be a simple, reliable transport layer between a FPGA and a host machine. It lets you configure a series of cores on a shared bus via a YAML or JSON file, and then provides a Python API to each core, along with vendor-agnostic Verilog HDL to instantiate them on your FPGA.

For more information check out the docs:
[https://fischermoseley.github.io/manta](https://fischermoseley.github.io/manta)
py-ddosX 1.1.2
🔗 Package Guide On https://github.com/mohamedlunar/py-ddos3
Requires Python: >=3.6

# PyDDOS X

A simple DDoS testing library in Python. It provides various attack methods such as UDP flood, SYN flood, and Botnet simulation for testing and simulation purposes.

## Features
- **UDP Flood**: Launch a UDP flood attack to test the resilience of a server.
- **SYN Flood**: Simulate a SYN flood attack.
- **Botnet**: Simulate a botnet attack by using multiple threads.

## Installation

You can install `pyddos3` via `pip` from PyPI:

```bash
pip install py-ddosX
```
boaHancock 1.0.6
This is tools to make starter template python for create moduls or library
Requires Python: >=3.6

# boaHancock
![Logo](.github/boa.jpg)

This is project to generate starter template for create moduls or library in python

## Feature

- create folder `poject_name` and `test`.
- create file `LICENSE` from license list selected.
- create file `README.md` from templates .
- make file `setup.py` configured to deployment.
- make file `.gitignore` for configure to deployment in github.
- Membuat file `__init__.py` di dalam folder `src` untuk modul Python.
- provide license like a MIT, Apache, BSD-3-Clause, e, g.

## Instalation
```bash
git clone https://github.com/Anammkh/boaHancock.git
cd boaHancock/boaHancock/
python generator.py
```
## or using pip
```
pip install boaHancock

boaHancock
```

##
capibara-model 1.1.6
Language model with contextual processing
Requires Python: >=3.8
Keywords: machine-learning nlp language-model tpu jax

# 🦫 CapibaraModel

Language model with contextual processing based on JAX/Flax.

## ✨ Features

- **🧠 Architecture**:
  - 🔄 Multi-head attention
  - 🎯 Contextual activation
  - 🔍 Coherence detection
  - 🎭 Personality management

- **⚡ Optimizations**:
  - 🚀 Native TPU support
  - ⚙️ Efficient processing
  - 📦 Optimized batching
  - 🕸️ Integrated sparsity

## 📋 Requirements

- Python >= 3.8
- JAX >= 0.4.1
- Flax >= 0.6.0
- Optax >= 0.1.3

## 🚀 Installation

```bash
pip install capibara_model
```

## 💻 Usage

### Basic Example

```python
from capibara_model.core.model import CapibaraModel
from capibara_model.core.inference import CapibaraInference

# Create model
model = CapibaraModel(hidden_size=768, num_heads=8)

# Create inference
inference = CapibaraInference(hidden_size=768)

# Generate response
response = inference("How are you?")
print(response)
```

### Advanced Example

```python
# Custom configuration
config = {
    'model': {
        'hidden_size': 1024,
        'num_heads': 16,
        'num_layers': 24,
        'dropout_rate': 0.1
    },
    'training': {
        'batch_size': 32,
        'learning_rate': 1e-4,
        'warmup_steps': 1000
    }
}

# Create model with custom config
model = CapibaraModel(**config['model'])

# Process conversation
context = "Previous conversation context..."
response = inference(
    "What's the meaning of life?",
    context=context
)
```

## ⚙️ Configuration

```python
config = {
    'hidden_size': 768,
    'num_heads': 8,
    'num_layers': 12,
    'dropout_rate': 0.1,
    'use_tpu': False
}
```

## 🛠️ Development

```bash
# Install development dependencies
pip install capibara_model[dev]

# Run tests
pytest tests/

# Run specific test
pytest tests/test_model.py -k "test_attention"

# Check code style
black capibara_model/
```

## 📝 License

MIT License. See `LICENSE` for more information.

## 📫 Contact

Marco Durán - [marco@anachroni.co](mailto:marco@anachroni.co)

[🌐 Website](https://www.anachroni.co) | [💻 GitHub](https://github.com/anachroni-io/CapibaraModel)

## 🙏 Acknowledgments

Special thanks to:

- JAX/Flax team
- TPU Research Cloud
- Open source community