semvec/pypi-packages huggingface dataset:
vllm-plugin-meralion2 0.1.4
A vLLM plugin to register the MERaLiON-2-10B model architecture with vLLM’s plugin system.
Requires Python: >=3.9

## MERaLiON2 vLLM Plugin

### Licence

[MERaLiON-Public-Licence-v3](https://huggingface.co/datasets/MERaLiON/MERaLiON_Public_Licence/blob/main/MERaLiON-Public-Licence-v3.pdf)

### Set up Environment

This vLLM plugin supports vLLM version `0.6.5` ~ `0.7.3` (V0 engine), and `0.8.5` ~ `0.8.5.post1` (V1 engine). 

Install the MERaLiON2 vLLM plugin.

```bash
pip install vllm-plugin-meralion2
```

It's strongly recommended to install flash-attn for better memory and gpu utilization. 

```bash
pip install flash-attn --no-build-isolation
```

### Offline Inference

Refer to [offline_example.py](https://huggingface.co/MERaLiON/MERaLiON-2-10B/blob/main/vllm_plugin_meralion2/offline_example.py) for offline inference example.

### OpenAI-compatible Serving

Refer to [openai_serve_example.sh](https://huggingface.co/MERaLiON/MERaLiON-2-10B/blob/main/vllm_plugin_meralion2/openai_serve_example.sh) for openAI-compatible serving example.

To call the server, you can refer to [openai_client_example.py](https://huggingface.co/MERaLiON/MERaLiON-2-10B/blob/main/vllm_plugin_meralion2/openai_client_example.py).

Alternatively, you can try calling the server with curl, refer to [openai_client_curl.sh](https://huggingface.co/MERaLiON/MERaLiON-2-10B/blob/main/vllm_plugin_meralion2/openai_client_curl.sh).

### Changelog

#### 0.1.4

- Fixed multi-audio handling for a single request.
- Fixed server-side internal failure when multiple requests with different audio chunk counts are batched together.
- Added more docstrings for better code readability and maintenance.

Full history: see [CHANGELOG.md](./CHANGELOG.md).
meru-os 1.0.0
The First Sovereign AI Operating System based on Sanskrit Logic
Requires Python: >=3.8

# 🕉️ Meru OS: The Sovereign AI Stack

Meru OS is the world's first **Sovereign AI Operating System** built on **Vedic Logic** (Panini's Grammar), **Prime Number Theory** (Pingala's Binary), and **Neuro-Symbolic Architecture**.

Meru OS represents a paradigm shift from "Black Box" probabilistic models to **"Glass Box" Deterministic Intelligence**. By encoding logic directly into the kernel using Prime Numbers, we achieve an AI that is **transparent, verifiable, and energy-efficient.**

## 🌱 Green AI: The Eco-Sattva Architecture
Meru OS is designed to be the world's most energy-efficient AI stack:
1.  **Reversible Computing**: The **Bija Hypervisor** tracks state changes as Prime Factors. This allows processes to be perfectly reversed (Pralaya) without entropy loss, theoretically approaching **Zero-Heat Computing** (Landauer's Limit).
2.  **Compressed Intelligence**: Using **Pingala's Chandas**, we run complex models on kilobytes of data (e.g., the Universal Corpus is just 190KB), drastically reducing training and inference costs.
3.  **Logical vs. Brute Force**: Instead of burning GPU cycles to "guess" the next token, **Panini's Grammar** derives it elegantly using invariant rules.

## 🇮🇳 The Sovereign Trinity

1.  **Shiva Kernel (`meru.shiva`)**: 
    -   The Bootloader.
    -   Runs directly on **Sanskrit Bytecode** (`.skt`).
    -   Only 39 bytes (The Maheshvara Sutras).

2.  **Bija Hypervisor (`meru.bija`)**:
    -   **Reversible Computing**.
    -   Translates Linux Syscalls into **Prime Factors**.
    -   Allows "Time Travel Debugging" (Pralaya).

3.  **Panini Intelligence (`meru.panini`)**:
    -   **The Glass Box**.
    -   A Neuro-Symbolic Model that **understands** language structure.
    -   Pre-loaded with the **Universal Corpus** (Sanskrit/Greek/German Cognates).
    -   **Zero Hallucination** on foundational truths.

## 🌍 The Universal Connection
Meru OS includes a compressed **Universal Language Stack** (190KB).
It proves that **Sanskrit, Greek, and German** share the same mathematical roots.

```python
import meru_os
model = meru_os.PaniniLLM()
print(model.query("MOTHER"))
# Output: ['mātṛ (Sanskrit)', 'mētēr (Greek)', 'mutter (German)']
```

## 🚀 Installation

```bash
pip install meru-os
```

## 📜 The Manifesto
We believe that AI Sovereignty comes not from owning GPU clusters, but from owning the **Logic**.
Meru OS is India's contribution to the future of computing—a future that is efficient, interpretable, and aligned with cosmic rhythms.

**Jaya Hind.** 🇮🇳
demandlib 0.2.3a1
Creating heat and power demand profiles from annual values
Requires Python: >=3.10

========
Overview
========

.. start-badges

.. list-table::
    :stub-columns: 1

    * - docs
      - |docs|
    * - tests
      - | |tox-pytest| |tox-checks| |coveralls|
    * - package
      - | |version| |wheel| |supported-versions| |supported-implementations| |commits-since| |packaging|


.. |tox-pytest| image:: https://github.com/oemof/oemof-demand/workflows/tox%20pytests/badge.svg
     :target: https://github.com/oemof/oemof-demand/actions?query=workflow%3A%22tox+checks%22

.. |tox-checks| image:: https://github.com/oemof/oemof-demand/workflows/tox%20checks/badge.svg?branch=dev
     :target: https://github.com/oemof/oemof-demand/actions?query=workflow%3A%22tox+checks%22

.. |packaging| image:: https://github.com/oemof/oemof-demand/workflows/packaging/badge.svg?branch=dev
     :target: https://github.com/oemof/oemof-demand/actions?query=workflow%3Apackaging

.. |docs| image:: https://readthedocs.org/projects/oemof-demand/badge/?style=flat
    :target: https://oemof-demand.readthedocs.io/
    :alt: Documentation Status

.. |coveralls| image:: https://coveralls.io/repos/oemof/oemof-demand/badge.svg?branch=dev&service=github
    :alt: Coverage Status
    :target: https://coveralls.io/github/oemof/oemof-demand?branch=dev

.. |version| image:: https://img.shields.io/pypi/v/oemof-demand.svg
    :alt: PyPI Package latest release
    :target: https://pypi.org/project/oemof-demand

.. |wheel| image:: https://img.shields.io/pypi/wheel/oemof-demand.svg
    :alt: PyPI Wheel
    :target: https://pypi.org/project/oemof-demand

.. |supported-versions| image:: https://img.shields.io/pypi/pyversions/oemof-demand.svg
    :alt: Supported versions
    :target: https://pypi.org/project/oemof-demand

.. |supported-implementations| image:: https://img.shields.io/pypi/implementation/oemof-demand.svg
    :alt: Supported implementations
    :target: https://pypi.org/project/oemof-demand

.. |commits-since| image:: https://img.shields.io/github/commits-since/oemof/oemof-demand/latest/dev
    :alt: Commits since latest release
    :target: https://github.com/oemof/oemof-demand/compare/master...dev



.. end-badges

Creating heat and power demand profiles from annual values.

* Free software: MIT license

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

::

    pip install oemof-demand

You can also install the in-development version with::

    pip install https://github.com/oemof/oemof-demand/archive/master.zip


Documentation
=============


https://oemof-demand.readthedocs.io/


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

To run all the tests run::

    tox

Note, to combine the coverage data from all the tox environments run:

.. list-table::
    :widths: 10 90
    :stub-columns: 1

    - - Windows
      - ::

            set PYTEST_ADDOPTS=--cov-append
            tox

    - - Other
      - ::

            PYTEST_ADDOPTS=--cov-append tox
FlaskBB 2.2.0
A classic Forum Software in Python using Flask.
Requires Python: >=3.12
Keywords: community, flask, forum, social

# FlaskBB

[![Build Status](https://github.com/flaskbb/flaskbb/actions/workflows/tests.yml/badge.svg)](https://github.com/flaskbb/flaskbb/actions/workflows/tests.yml)
[![License](https://img.shields.io/badge/license-BSD-blue.svg)](https://https://github.com/flaskbb/flaskbb)
[![#flaskbb:matrix.org](https://img.shields.io/badge/[matrix]-%23flaskbb%3Amatrix.org-blue)](https://matrix.to/#/#flaskbb:matrix.org)

*FlaskBB is a Forum Software written in Python using the micro framework Flask.*

Currently, following features are implemented:

* Private Messages
* Admin Interface
* Group based permissions
* Markdown Support
* Topic Tracker
* Unread Topics/Forums
* i18n Support
* Completely Themeable
* Plugin System
* Command Line Interface


## Quickstart

For a complete installation guide please visit the installation documentation
[here](https://flaskbb.readthedocs.org/en/latest/installation.html).

This is how you set up an development instance of FlaskBB:

* Create a virtualenv
* Configuration
    * `make devconfig`
* Install dependencies and FlaskBB
    * `make install`
* Run the development server
    * `make run`
* Visit [localhost:5000](http://localhost:5000)


## License

FlaskBB is licensed under the [BSD License](https://github.com/flaskbb/flaskbb/blob/master/LICENSE).


# Links

* [Project Website](https://github.com/flaskbb/flaskbb)
* [Documentation](https://flaskbb.readthedocs.io)
* [Source Code](https://github.com/flaskbb/flaskbb)
NuCS 9.1.2
A Numpy and Numba based Python library for solving Constraint Satisfaction Problems over finite domains
Requires Python: >=3.11

![NucS logo](https://raw.githubusercontent.com/yangeorget/nucs/main/assets/nucs.png)


![pypi version](https://img.shields.io/pypi/v/nucs?color=blue&label=pypi%20version&logo=pypi&logoColor=white)
![pypi downloads](https://img.shields.io/pypi/dm/NUCS)

![python version](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fyangeorget%2Fnucs%2Fmain%2Fpyproject.toml)
![numba version](https://img.shields.io/badge/numba-v0.64-blue)
![numpy version](https://img.shields.io/badge/numpy-v2.4.2-blue)

![tests](https://github.com/yangeorget/nucs/actions/workflows/test.yml/badge.svg)
![doc](https://img.shields.io/readthedocs/nucs)
![license](https://img.shields.io/github/license/yangeorget/nucs)

## TLDR
NuCS is a Python library for solving Constraint Satisfaction and Optimization Problems.
Because it is 100% written in Python, 
NuCS is easy to install and allows to model complex problems in a few lines of code.
The NuCS solver is also very fast because it is powered by [Numpy](https://numpy.org/) and [Numba](https://numba.pydata.org/).

## Installation
```bash
pip install nucs
```
## Documentation
Check out [NuCS documentation](https://nucs.readthedocs.io/).

## With NuCS, in a few seconds you can ...
### Find all 14200 solutions to the [12-queens problem](https://www.csplib.org/Problems/prob054/)
```bash
NUMBA_CACHE_DIR=.numba/cache python -m nucs.examples.queens -n 12
```
![queens](https://raw.githubusercontent.com/yangeorget/nucs/main/assets/queens.gif)

### Compute the 92 solutions to the [BIBD(8,14,7,4,3) problem](https://www.csplib.org/Problems/prob028/)
```bash
NUMBA_CACHE_DIR=.numba/cache python -m nucs.examples.bibd -v 8 -b 14 -r 7 -k 4 -l 3
```
![bibd](https://raw.githubusercontent.com/yangeorget/nucs/main/assets/bibd.gif)

### Demonstrate that the optimal [10-marks Golomb ruler](https://www.csplib.org/Problems/prob006/) length is 55
```bash
NUMBA_CACHE_DIR=.numba/cache python -m nucs.examples.golomb -n 10
```
![golomb](https://raw.githubusercontent.com/yangeorget/nucs/main/assets/golomb.gif)
cnrgh-dl 2.0.0
Client de téléchargement compatible avec l'authentification à double facteur (2FA) requise par le portail de téléchargement du CNRGH.
Requires Python: >=3.10
Keywords: CNRGH, download, projects, client, 2FA

# cnrgh-dl

`cnrgh-dl` (*CNRGH Download*) est un client de téléchargement compatible avec l'**authentification à double facteur** (2FA) requise par le portail de téléchargement du **CNRGH**.

Sa documentation complète (prérequis, installation, utilisation...) est consultable via le lien suivant : https://www.cnrgh.fr/data-userdoc/download_with_client/.
django-ai-support 0.1.11
AI support for your django app.
Requires Python: >=3.10

# django-ai-support

This library is powered by langchain and langgraph to make easy AI support for your online shop or any website you want to create with Django.

[This is used Agent workflow](https://langchain-ai.github.io/langgraph/tutorials/workflows/#agent)

## Get started

### settings

Settings should be something like this 

```python
AI_SUPPORT_SETTINGS = {
    "TOOLS": [],
    "SYSTEM_PROMPT": "You are the supporter of a bookstore website.",
    "LLM_MODEL": model,
}
```

TOOLS: tools are for your AI model.

SYSTEM_PROMPT: This is important for your AI support, but the default prompt is: ```You are the supporter of a bookstore website.``` As you see.

LLM_MODEL: your chat model.  like this:

```python
model = init_chat_model("gemini-2.5-flash", model_provider="google_genai")
```

[see more](https://python.langchain.com/docs/integrations/chat/)

*Be careful*: LLM_MODEL can not be None.

### config

Additionally, you can integrate your tools within your apps and add them to `TOOLS`.

For example, I have an app with the name of book. and this is inside of my `tools.py` file:

```python
from langchain_core.tools import tool

from .models import Book

@tool(description="this is to get list of book with a price")
def get_books_with_price(price:int) -> str:
    """
    get book with input price

    Args:
        price (int): price. in dollars

    Returns:
        str: list of books.
    """
    text = ""

    for book in Book.objects.filter(price=price).select_related("author"):
        book_detail = f"book name: {book.name}, book price: {book.price}, Author: {book.author.first_name} {book.author.last_name}"
        text += book_detail + "\n"

    return text


```

After that, I can add this tool easily. This is inside my `apps.py` file:

```python
from django.apps import AppConfig


class BookConfig(AppConfig):
    default_auto_field = 'django.db.models.BigAutoField'
    name = 'book'

    def ready(self):
        from .tools import get_books_with_price as  my_tool
        from django_ai_support.conf import append_tools

        append_tools([my_tool])


```

[read more about tool calling in langchain](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/#dynamically-select-tools)


### API

Now, you can use the chat API to talk with your AI support:

```python
from django.urls import path

from django_ai_support.views import ChatAiSupportApi

urlpatterns = [
    path("ai/", ChatAiSupportApi.as_view())
]
```

![swagger](./images/swagger-example.png)