semvec/pypi-packages huggingface dataset:
cvxreg 0.2.2
A Python Package for Convex Regression
License: MIT
Requires Python: >=3.7
Keywords: ML, Prediction, Regression

[![New release](https://img.shields.io/github/v/release/ConvexRegression/cvxreg?display_name=tag&label=Lastest&color=%234B78E6)](https://github.com/ConvexRegression/cvxreg/releases)
[![Documentation Status](https://readthedocs.org/projects/cvxreg/badge/?version=latest)](https://cvxreg.readthedocs.io/en/latest/?badge=latest)
[![PyPI downloads](https://img.shields.io/pypi/dm/cvxreg.svg?maxAge=21600)](https://pypistats.org/packages/cvxreg)

**cvxreg** is a Python package for machine learning with convex regression models built on [`CVXPY`](https://www.cvxpy.org). 

The core aims of this package are:
* make convex regression models "easy to call" from Python,
* interface with [`CVXPY`](https://www.cvxpy.org),
* focus on a "machine learning" perspective, i.e.: predictive task, hyper-parameters should be obtained by a data-driven method such as cross-validation.

## Installation

The [`cvxreg`](https://pypi.org/project/pycreg) package is now available on PyPI and the latest development version can be installed from the GitHub repository [`ConvexRegression`](https://github.com/ConvexRegression/cvxreg). Please feel free to download and test it. We welcome any bug reports and feedback.

#### PyPI 

    pip install cvxreg
langchain-gradient 0.1.24
An integration package connecting Digitalocean and LangChain
License: MIT
Requires Python: <4.0,>=3.9

# langchain-gradient  
[![PyPI Downloads](https://static.pepy.tech/badge/langchain-gradient)](https://pepy.tech/projects/langchain-gradient)

This package contains the LangChain integration with DigitalOcean

## Installation

```bash
pip install -U langchain-gradient
```

And you should configure credentials by setting the `DIGITALOCEAN_INFERENCE_KEY` environment variable:

1. Log in to the DigitalOcean Cloud console
2. Go to the **Gradient Platform** and navigate to **Serverless Inference**.
2. Click on **Create model access key**, enter a name, and create the key.
3. Use the generated key as your `DIGITALOCEAN_INFERENCE_KEY`:   


Create .env file with your access key:  
```DIGITALOCEAN_INFERENCE_KEY=your_access_key_here```

## Chat Models

`ChatGradient` class exposes chat models from langchain-gradient.

### Invoke

```python
import os
from dotenv import load_dotenv
from langchain_gradient import ChatGradient

load_dotenv()

llm = ChatGradient(
    model="llama3.3-70b-instruct",
    api_key=os.getenv("DIGITALOCEAN_INFERENCE_KEY")
)

result = llm.invoke("What is the capital of France?.")
print(result)
```

### Stream

```python
import os
from dotenv import load_dotenv
from langchain_gradient import ChatGradient

load_dotenv()

llm = ChatGradient(
    model="llama3.3-70b-instruct",
    api_key=os.getenv("DIGITALOCEAN_INFERENCE_KEY")
)

for chunk in llm.stream("Tell me what happened to the Dinosaurs?"):
    print(chunk.content, end="", flush=True)
```

More features coming soon.