semvec/pypi-packages huggingface dataset:
sec-share 0.1.4
A CLI tool to securely share code snippets with automatic secret detection and redaction
Requires Python: >=3.7

# SecShare

A command-line tool for securely sharing code snippets with automatic secret detection and redaction.

## Installation

```bash
pip install sec-share
```

## Usage

```bash
# Share a file
sec-share -t "My Code" -l language my_script

# Share from stdin
cat my_script.py | sec-share -t "My Code" -l python
```

## Features

- Automatic detection and redaction of sensitive information
- Support for multiple programming languages
- Secure sharing with expiration dates
- Easy-to-use command-line interface

## License

MIT License
irrectum 0.1.2
Metrics for Evaluating Model Quality
Requires Python: >=3.10

# Irrectum - Metrics for Evaluating Model Quality

## Description
Irrectum provides a set of metrics for evaluating the quality of machine learning model predictions. These metrics include both standard and specialized indicators that help analyze model performance and identify its strengths and weaknesses.

### Features
- **RMSE (Root Mean Square Error)**: Assesses the error of the model's values relative to the actual values.
- **NRMSE (Normalized RMSE)**: Normalizes RMSE, allowing for comparison of models with different scales.
- **Pearson Correlation Coefficient**: Measures the degree of linear dependence between predicted and actual values.
- **Covariance**: Evaluates how two variables change together.
- **Coefficient of Determination (R²)**: Shows the proportion of variance in the dependent variable explained by the model.
- **Adjusted R²**: Takes into account the number of predictors in the model, allowing for comparison of models with different numbers of factors.
- **Residual Sum of Squares (RSS)**: Assesses the total error of the model.
- **MAPE (Mean Absolute Percentage Error)**: Measures the percentage error of the model relative to the original.
- **MAE (Mean Absolute Error)**: Evaluates the average error between predicted and actual values.
- **AIC & BIC**: Information criteria for comparing models considering their complexity.

## Installation
To install the necessary dependencies, use the following command:

```bash
pip install irrectum
```


Example of using the metrics:
``` python
import numpy as np
from abc_metrics import RMSE, MAE

# Example data
testTarget = np.array([3, -0.5, 2, 7])
testPrediction = np.array([2.5, 0.0, 2, 8])

# Creating instances of the metrics
rmse_metric = RMSE()
mae_metric = MAE()

# Evaluating model quality
rmse_value = rmse_metric.test(testTarget, testPrediction)
mae_value = mae_metric.test(testTarget, testPrediction)

print(f"RMSE: {rmse_value}, MAE: {mae_value}")
```

## Support
If you have any questions or need assistance, please create an issue in the repository.

## Contributing
We welcome contributions! Please refer to our contribution guidelines.

## Authors and Acknowledgment
Show your appreciation to those who have contributed to the project.

## License
This project is licensed under the MIT License.
savant-models 1.0.13
A small example package
Requires Python: >=3.9

# Savant Pydantic Models Python Package

This package is a library for all the pydantic models and enums used by the SAVANT monolith

## Table of Contents 
- [Installation]
- [Prerequisites]
- [Documentation]
- [Usage]
- [Contributing]
- [License]

## Installation

The pip package can be found on the PyPI website: https://pypi.org/project/savant-models/1.0.0/ where you can copy the command for pip installation. 

## Prerequisites 
- Python3

## Documentation 
The `PostProfileTag` in the monolith (app.profile_management.request_bodies.post_profile_tag) is the same model as the PostedProfileTag in this pip package. A duplicate model has been added to this package with a different name to avoid complications with model dependencies.

## Usage 
- SAVANT Monolith 

## Contributing
SAVANT Team

## License 

https://opensource.org/license/mit
huggingface-togetherai 1.1.2
Add your description here
Requires Python: <3.13,>=3.10

# 🚀 Huggingface-TogetherAI LangChain Wrapper

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python Version](https://img.shields.io/badge/python-3.11%2B-blue.svg)](https://www.python.org/)

A **LangChain** integration for **DeepSeek-R1** and **Meta Llama-3.3-70B-Instruct-Turbo** models via **Hugging Face's Inference API**, enabling seamless interaction with state-of-the-art language models.

## ✨ Features

- 🚀 **Custom LangChain Chat Model** – Optimized for Hugging Face + Together AI.
- ⚡ **Sync & Async Support** – Run queries in synchronous or asynchronous mode.
- 🌊 **Streaming Capabilities** – Supports token streaming for real-time responses.
- 🛠️ **Tool Calling & Structured Output** – Enables function calling and JSON outputs.
- 🔧 **Configurable Model Parameters** – Fine-tune temperature, max tokens, etc.

## 📦 Installation

```bash
pip install huggingface-togetherai 
```

## 🚀 Quick Start

```python
from huggingface_togetherai import ChatHuggingFaceTogetherAI

hf_token = "your_huggingface_token"
hf_llm = ChatHuggingFaceTogetherAI(
    model="deepseek-ai/DeepSeek-R1",
    hf_token=hf_token
)

response = hf_llm.invoke("Hi!")
print(response)
```

## 🤔 Why Use Huggingface-TogetherAI?

In **LangChain**, the `HuggingFaceEndpoint` class is typically used for Hugging Face models:

```python
from langchain_huggingface import HuggingFaceEndpoint
from langchain_huggingface.chat_models import ChatHuggingFace

hf_endpoint = HuggingFaceEndpoint(
    repo_id="deepseek-ai/DeepSeek-R1",
    task="text-generation",
    huggingfacehub_api_token=hf_token
)

langchain_llm = ChatHuggingFace(llm=hf_endpoint)
langchain_llm.invoke("Hello")
```

However, this results in an error:

```
The model deepseek-ai/DeepSeek-R1 is too large to be loaded automatically (688GB > 10GB).
```

### ✅ The Better Alternative: Huggingface-TogetherAI

With **Huggingface-TogetherAI**, you can seamlessly use large models without running into memory issues:

```python
from huggingface_togetherai import ChatHuggingFaceTogetherAI

hf_llm = ChatHuggingFaceTogetherAI(
    model="deepseek-ai/DeepSeek-R1",
    hf_token=hf_token,
    other_params...
)

response = hf_llm.invoke("Hello")
print(response)  # Output: '<think>\n\n</think>\n\nHello! How can I assist you today? 😊'
```

### 🎉 Good News!

✅ You can leverage **all [Langchain](https://www.langchain.com/) functionalities** for standard LLMs with this package.

## 📜 License

This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for more details.
ally-ai-core 0.7.0
Core package for ally
Requires Python: >=3.8

# Ally AI Core

## How to use

### Config File

```yaml
llm:
  api_key: '<private-key>'
  api_version: "<api-version>"
  endpoint: "<endpoint>"
  model: "<model-name>"
  deployment_name: '<deployment-name>'
  temperature: 0.7
  streaming: true

embeddings:
  api_key: '<private-key>'
  api_version: "<api-version>"
  endpoint: "<endpoint>"
  model: "<model-name>"
  deployment_name: '<deployment-name>'
```



### Use Custom Settings

```yaml
my_llm:
  api_key: '<private-key>'
  api_version: "<api-version>"
```

```python
from ally_ai_core import Settings

settings = Settings(section='my_llm')

```

### Override Settings

```python
from ally_ai_core import Settings

settings = Settings(section='my_llm', api_key='<new-api-key>')
```