semvec/pypi-packages huggingface dataset:
synthetica-py 0.1.3
Production-ready synthetic data generator for LLM training
Requires Python: >=3.9
Keywords: synthetic-data, llm, fine-tuning, machine-learning, data-generation, ai-training

# Synthetica-py 🚀

[![Latest Version](https://img.shields.io/pypi/v/synthetica-py.svg?color=blue&style=for-the-badge)](https://pypi.org/project/synthetica-py/)
[![Python Versions](https://img.shields.io/pypi/pyversions/synthetica-py.svg?style=for-the-badge)](https://pypi.org/project/synthetica-py/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=for-the-badge)](https://opensource.org/licenses/MIT)

**Synthetica-py** is a high-performance synthetic data generation engine. Designed for the 2026 AI landscape, it bridges the gap between local models (Ollama, LM Studio) and frontier APIs (Gemini 3 Flash).

---

### 🌟 New in v0.1.3
*   **Bulletproof JSON Parsing:** Advanced regex extraction and cleanup for inconsistent LLM responses.
*   **Enhanced Rate Limiting:** Smarter backoff and request pacing for Gemini Free Tier (10 RPM cap).
*   **Zero-Config Mode:** TaskSpec now supports sane defaults for instant data generation.

## 🚀 Quick Start

### Installation
```bash
pip install synthetica-py
```

### Ultra-Fast Usage
```python
import asyncio
from synthetica import SyntheticDataGenerator
from synthetica.core.task_spec import TaskSpec

async def main():
    # Uses gemini-3-flash-preview by default
    gen = SyntheticDataGenerator.with_gemini()
    
    task = TaskSpec(
        instruction="Generate healthy recipes",
        fields=["dish_name", "calories", "ingredients"],
        num_samples=5
    )
    
    dataset = await gen.generate_async(task)
    dataset.export_csv("recipes.csv")

if __name__ == "__main__":
    asyncio.run(main())
```

## 🛠 Features

-   **🎯 Task-Centric:** Specialized logic for Instruction Tuning, RAG Evaluation (Grounding), AGENT Trajectories, and Adversarial Safety testing.
-   **⚖️ Curriculum Difficulty:** Automatic calibration of difficulty levels (Easy -> Adversarial) to improve model convergence.
-   **🛠 Model Provider Agnostic:** Unified API for **Ollama**, **LM Studio**, **Google Gemini**, and **llama.cpp**.
-   **🔍 Multi-Stage Pipeline:** Automated generation -> Cross-model Critique -> Schema Validation -> Semantic Deduplication.
-   **📦 Cloud-Ready Export:** Native support for HuggingFace `datasets`, JSONL, and flattened CSV formats.

## 👨‍💻 Developer
**C. Emre Karataş**

## 📄 License
MIT License - Developed for the open-source AI community.
m1n1-proxyclient 0.4.0
M1N1 proxy client for interacting with Apple Silicon devices
Requires Python: >=3.11

# M1N1 Proxy Client

A Python package for interacting with Apple Silicon devices using the M1N1 proxy.

## Installation

Install from PyPI:

```bash
pip install m1n1-proxyclient
```

Or from source:

```bash
poetry install
```

## Usage

[Add usage examples here]

## License

This project is licensed under the MIT License - see the LICENSE file for details.

Copyright The Asahi Linux Contributors
ncert-dl 1.0.2
NCERT textbook downloader for Classes 6–12
Requires Python: >=3.9

# ncert-dl

**ncert-dl** is a command-line tool that downloads official NCERT textbooks directly from `ncert.nic.in` for Class 10 (and partially Class 9). It organizes them into clean folders with human-readable chapter names.

## Features

*   **Official Source**: Downloads directly from the NCERT website.
*   **Interactive UI**: Select class, subjects, and language using a checkbox menu.
*   **Smart Renaming**: Automatically converts cryptic filenames (e.g., `jemh101.pdf`) to readable ones (e.g., `Mathematics/01_Chapter.pdf`).
*   **Clean Organization**: structured folders by Subject > Chapter.
*   **Resumable**: Skips already downloaded files and handles interruptions gracefully.
*   **Cross-Platform**: Works on Windows, macOS, and Linux.

## Installation

```bash
pipx install ncert-dl
```

Or with pip:

```bash
pip install ncert-dl
```

## Usage

Simply run the command:

```bash
ncert-dl
```

Follow the interactive prompts:
1.  **Select Class** (e.g., 10)
2.  **Select Subjects** (e.g., Mathematics, Science)
3.  **Select Language** (English or Hindi)

The tool will download the PDFs and organize them in a `NCERT_Downloads` directory.

## How It Works

NCERT uses a predictable naming convention for their PDF files. This tool reverses that convention to generate URLs dynamically.

*   **Maths/Science**: `[prefix][class][chapter].pdf` (e.g., `jemh101.pdf`)
*   **Social Science**: Specific mappings (e.g., `jess101.pdf` -> History Ch 1)

This ensures the downloader is fast, reliable, and doesn't rely on web scraping.

## Disclaimer

This tool is for educational purposes only. It effectively automates the manual process of downloading textbooks from the official portal. Please respect copyright and terms of service of the source website.
jupyter-retext-language-server 1.0.1
A language server for Markdown documents that supports retext plugins

# jupyter-retext-language-server

A [retext] language server for [Jupyter LSP]. This builds upon the [unified-language-server] by exposing a simple configuration mechanism.

## Plugins

By default, the jupyter-retext-language-server ships with pre-installed with the `retext-spell` plugin out of the box.

## Extending

New retext plugins can be defined through Python entry points, e.g.

```toml
[project.entry-points.jupyter_retext_language_server_plugin_v1]
retext-spell = "jupyter_retext_language_server.plugins:retext_spell"
```

The `retext_spell` name in the `jupyter_retext_language_server.plugins` module has the following form:

### Entry points

```python
retext_spell = {
    "path": /path/to/retext-spell.mjs",
    "properties_schema": {
        "retext.plugins.retext-spell.enabled": {
            "type": "boolean",
            "default": True,
            "description": "Enable retext-spell plugin.",
        },
        // ... etc
    }
}

```

It is through this entry point that the settings schema and path to the ESM module can be defined.

### ESM plugin

The ESM plugin exposed by the Python entry point must define two named exports, e.g.

```javascript
export const name = "retext-spell";

export async function plugin(spellConfig) {
  // Plugin impl
  return (tree, file, next) => {};
}
```

These define the name (which maps to the plugin name in the properties schema), and the plugin implementation that is invoked by the plugin manager.

## Future development ideas

- Generalise the project to allow multiple ecosystems (unified, retext, etc).
- Add proper schema merging.
- Validate plugin subschemas
- Validate plugin ESM modules
- Implement upstream fixes to expose the config — https://github.com/unifiedjs/unified-language-server/issues/69

[unified-language-server]: https://github.com/unifiedjs/unified-language-server
[retext]: https://github.com/retextjs/retext
[retext-spell]: https://github.com/retextjs/retext-spell
[jupyter lsp]: https://github.com/jupyter-lsp/jupyterlab-lsp
solidago 0.5.0
A toolbox for Solid Algorithmic Governance
Requires Python: >=3.13
Keywords: collaborative recommendations, comparison based, judgement aggregation, mehestan, tournesol

# Solidago

[![PyPi](https://img.shields.io/pypi/v/solidago)](https://pypi.org/project/solidago)
[![License](https://img.shields.io/pypi/l/solidago.svg)](https://github.com/tournesol-app/tournesol/tree/main/solidago#copyright--license)
[![CI status](https://github.com/tournesol-app/tournesol/actions/workflows/solidago-test.yml/badge.svg)](https://github.com/tournesol-app/tournesol/actions/workflows/solidago-test.yml)

A toolbox for **Solid** **A**lgorithmic **Go**vernance, used by the Tournesol platform.

**Documentation**: https://solidago.tournesol.app

---

## Development

### Tests

To run tests, setup a virtual environment, activate it, install dependencies and run pytest.
```sh
python3 -m venv devenv
source devenv/bin/activate
pip install -e .[test]
pytest
```
Depending on the platform, you may need to replace ```source devenv/bin/activate``` by another call.
See the [venv doc](https://docs.python.org/3/library/venv.html#how-venvs-work) for more information.

### Docs

The documentation website (deployed to https://solidago.tournesol.app) is built using [MkDocs](https://www.mkdocs.org/) and [Material for MkDocs](https://squidfunk.github.io/mkdocs-material/).

To build the documentation locally:

1. Install the "docs" dependencies in your virtual environment
```sh
pip install -e .[docs]
```

2. Start the MkDocs development server
```sh
mkdocs serve -a localhost:9000
```
Find more options on https://www.mkdocs.org/user-guide/cli/


## Synthetic experiments

To run experiments, once the virtual environement setup, you may run the experiments on synthetic data using.
```
python3 experiments/synthetic.py experiments/resilience.json
python3 experiments/synthetic.py experiments/engagement_bias.json
```
The results will be exported in ```experiments/results```.
You may modify the experiments by editing ```experiments/resilience.json```, 
or by creating a new ```.json``` file.


## Publish a new release

1. In a Pull Request, update the version number in [`./src/solidago/__version__.py`](./src/solidago/__version__.py)
2. The package will be published automatically when the new version is merged into "main", by [this Github Action](../.github/workflows/solidago-publish.yml).


## Copyright & License

Copyright 2023-2025 Tournesol Association and contributors.

This program is free software: you can redistribute it and/or modify it under the terms of the [**GNU Lesser General Public License**](./LICENSE.LESSER) as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with this program. If not, see https://www.gnu.org/licenses/.
magnopy 0.4.3
Magnopy is a Python package for magnons.
Requires Python: >=3.9

*******
Magnopy
*******

Magnopy is a Python package for the magnon-related properties:

* Ground state energy
* Energy minimization
* Linear Spin Wave theory and what comes from it

Please visit an extensive documentation on `magnopy.org <https://magnopy.org>`_ to find
out more.

Installation
============

To install magnopy run (you may need to use ``pip3``):

.. code-block:: console

    pip install magnopy

Usage
=====

There are two ways to use magnopy:

* As a blackbox via `command line interface <https://docs.magnopy.org/en/latest/user-guide/index.html>`_
* As a `Python library <https://docs.magnopy.org/en/latest/user-guide/index.html>`_

User support
============

There are two ways to ask for help or give feedback

*   `Forum at google groups <https://groups.google.com/g/magnopy>`_

    You may send email directly to magnopy@googlegroups.com

*   `Github discussions <https://github.com/magnopy/magnopy/discussions>`_

    You may need to create an account at github to post a question.

Both are monitored by the authors, choose the one you prefer.

Development
===========

We welcome contributions to magnopy. If you would like to contribute, please
consult the `development guide <https://docs.magnopy.org/en/latest/development/index.html>`_
or contact the authors of the project.