semvec/pypi-packages huggingface dataset:
vishwamai 0.1.0
A math-focused machine learning library with efficient quantization and advanced tokenization
Requires Python: >=3.8

# VishwamAI

VishwamAI is a sophisticated machine learning library focusing on efficient model quantization, advanced tokenization, and mathematical reasoning capabilities.

## Features

- **Advanced Tokenization**: Conceptual tokenizer with semantic clustering and special token handling
- **Efficient Quantization**: Support for FP8 and BF16 quantization
- **Mathematical Reasoning**: Integration with GSM8K dataset for advanced mathematical problem-solving
- **Model Architecture**: Flexible transformer-based architecture with configurable parameters
- **Training Utilities**: Support for distributed training, mixed precision, and gradient accumulation

## Installation

```bash
pip install -e .
```

## Quick Start

```python
from vishwamai.model import VishwamaiModel
from vishwamai.conceptual_tokenizer import ConceptualTokenizer

# Initialize tokenizer and model
tokenizer = ConceptualTokenizer()
model = VishwamaiModel()

# Example usage
text = "Solve: If John has 5 apples and gives 2 to Mary, how many does he have left?"
tokens = tokenizer.encode(text)
output = model.generate(tokens)
```

## Testing

Run the test suite:

```bash
pytest -v
```

## Requirements

- Python >= 3.8
- PyTorch >= 2.1.0
- CUDA toolkit (for GPU support)
- Additional dependencies listed in setup.py

## Project Structure

```
vishwamai/
├── conceptual_tokenizer.py   # Advanced tokenization implementation
├── kernel.py                 # CUDA kernels and quantization
├── model.py                 # Core model architecture
├── training.py              # Training utilities
└── configs/                 # Model configurations
```

## License

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

## Contributing

Contributions are welcome! Please read our contributing guidelines before submitting pull requests.
topic-autolabel 0.1.6
Automatic topic labeling using LLMs
Requires Python: >=3.10

# topic-autolabel

[![Documentation Status](https://readthedocs.org/projects/llama-cpp-python/badge/?version=latest)](https://llama-cpp-python.readthedocs.io/en/latest/?badge=latest)
[![License](https://img.shields.io/badge/License-Apache-green.svg)](LICENSE)
[![Python Version](https://img.shields.io/badge/python-3.10%20|%203.11%20|%203.12-blue.svg)](https://www.python.org)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Ruff](https://camo.githubusercontent.com/bb88127790fb054cba2caf3f3be2569c1b97bb45a44b47b52d738f8781a8ede4/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d68747470733a2f2f7261772e67697468756275736572636f6e74656e742e636f6d2f636861726c6965726d617273682f727566662f6d61696e2f6173736574732f62616467652f76312e6a736f6e)](https://github.com/charliermarsh/ruff)

Given text data, generates labels to classify the data into a set number of topics completely unsupervised.

## Example usage:

First, install the package with pip: ```pip install topic_autolabel```

```
# Labelling with supplied labels
from topic_autolabel import process_file
import pandas as pd

df = pd.read_csv('path/to/file')
candidate_labels = ["positive", "negative"]

# labelling column "review" with "positive" or "negative"
new_df = process_file(
    df=df,
    text_column="review",
    candidate_labels=candidate_labels,
    model_name="meta-llama/Llama-3.1-8B-Instruct" # default model to pull from huggingface hub
)
```

Alternatively, one can label text completely unsupervised by not providing the ```candidate_labels``` argument

```
from topic_autolabel import process_file
import pandas as pd

df = pd.read_csv('path/to/file')

# labelling column "review" with open-ended labels (best results when dataset talks about many topics)
new_df = process_file(
    df=df,
    text_column="review",
    model_name="meta-llama/Llama-3.1-8B-Instruct",
    num_labels=5 # generate up to 5 labels for each of the rows
)
```

## Ollama integration:

Provided you have an ollama server running, you can pass in the tag of the model you want to use to generate labels.

```
from topic_autolabel import process_file
import pandas as pd

df = pd.read_csv('path/to/file')

# labelling column "review" with open-ended labels, using llama3.1 hosted with ollama (llama 3.1 must be running, run ollama ps to verify)
new_df = process_file(
    df=df,
    text_column="review",
    model_name="llama3.1",
    num_labels=5 # generate up to 5 labels for each of the rows
)
```
pyutils-sqlmodel 0.2.4
Configuracion inicial hacia conexion a base de datos con SQLModel y un repositorio con las operaciones CRUD utilizando genericos para el ahorro de codigo
Requires Python: >=3.6

# PyUtils SQLModel

Este repositorio contiene utilidades para trabajar con SQLModel en Python. Proporciona herramientas y ejemplos para facilitar la interacción con bases de datos PostgreSQL utilizando SQLModel.

## Autor

Este proyecto fue creado por Christian Carballo Cano. Puedes encontrar más información sobre mí en mi [LinkedIn](https://www.linkedin.com/in/cano2908/).

## Configuración de la conexión a PostgreSQL

Para configurar la conexión a la base de datos PostgreSQL, necesitas definir las siguientes variables de entorno:

```env
SGDB - sqlite, mysql, postgresql
USER_DB - username
PASS_DB - password
HOST_DB - hostname o ip addres
PORT_DB - port number
NAME_DB - database name
```

Asegúrate de que estas variables estén correctamente configuradas antes de ejecutar las utilidades proporcionadas en este repositorio.

## Instalación

Para instalar las dependencias necesarias, ejecuta:

```bash
pip install -r requirements.txt
```

## Uso

Puedes encontrar ejemplos de uso en el directorio `database/core/model_repository`. Asegúrate de revisar y adaptar los ejemplos según tus necesidades específicas.

## Licencia

Este proyecto está licenciado bajo los términos de la licencia MIT. Consulta el archivo `LICENSE` para obtener más información.
blemli-mail 0.0.20
quickly send a mail to problemli (or to someone else)
Requires Python: >=3.8

# blemli_mail
quickly send a mail to problemli (or someone else)

## usage
easy:
```python
import blemli_mail
blemli_mail.send("message")
```

or fancy:
```python
import blemli_mail
blemli_mail.send("message",subject="Testmail", level="INFO",recipient="xyz@abc.com")
```

you have to add this to your .env:
```python
BM_SERVER="server.example.com"
BM_USER="user@example.com"
BM_PASSWORD="******************"
BM_SERVICE="servicename"
BM_RECIPIENT="mail@example.com"
```