semvec/pypi-packages huggingface dataset:
django-easymde 1.0.4
django-easymde is a WYSIWYG markdown editor for Django
License: MIT
Keywords: django, admin, wysiwyg, markdown, editor, easymde

# A markdown editor (with preview) for Django
Use a simple markdown editor https://github.com/Ionaru/easy-markdown-editor in django forms. This project is inspired by https://github.com/sparksuite/simplemde-markdown-editor and https://github.com/douglasmiranda/django-wysiwyg-redactor/.

***Note that [SimpleMDE](https://github.com/sparksuite/simplemde-markdown-editor) is no longer in development, and has been forked to create [EasyMDE](https://github.com/Ionaru/easy-markdown-editor), which is in active development as of mid-2024.***

# Getting started
* install django-easymde
```
pip install django-easymde
```

* add 'easymde' to INSTALLED_APPS.

```python
INSTALLED_APPS = (
    # ...
    'easymde',
    # ...
)
```

# Using field in models
```python
from django.db import models
from easymde.fields import EasyMDEField

class Entry(models.Model):
    title = models.CharField(max_length=250, verbose_name=u'Title')
    content = EasyMDEField(verbose_name=u'mardown content')
```

Note: The widget `EasyMDEWidget` can also be used in forms on existing fields.

# EasyMDE options
EasyMDE options can be set in `settings.py`:

```python
EASYMDE_OPTIONS = {
    'placeholder': 'Type here...',
    'status': False,
    'autosave': {
        'enabled': True
    }
}
```

***For the autosave option, this plugin will generate uniqueId with python's uuid.uuid4 automatically.***

Right now, this plugin supports [EasyMDE Configurations](https://github.com/Ionaru/easy-markdown-editor#configuration), but only the static ones(no support for javascript configurations such as ```previewRender```)

# Get EasyMDE instance from DOM

After EasyMDE is initialized, a EasyMDE instance can be retrieved from the DOM element:

```javascript
$('.easymde-box')[0].EasyMDE
```
scalegpt 1.6
Scalable GPT implementation using Distributed Data Parallel for efficient, multi-GPU training of transformer models.
License: MIT

# GPT From Scratch

**GPT From Scratch** is an open-source implementation of a GPT-style transformer model, designed for scalability and efficiency. By leveraging Distributed Data Parallel (DDP) training, this project allows for efficient multi-GPU training, making it easier to train large-scale models on extensive datasets.

## Features

- Built from scratch GPT-style transformer model
- Efficient multi-GPU training using PyTorch's Distributed Data Parallel (DDP)
- Scalable and optimized for large datasets
- Easy-to-use interface for training and inference

## Results
Our model outperforms the GPT-2 checkpoint values on the HellaSwag benchmark, demonstrating the effectiveness of our implementation.

### Model Output
Model Before Training: 
```
On a clear day indis DNSヘラ ignore Happ Ce Croatian mugVAavorable303 wayomb prom bartender surmia pass standingotoshanMore intensely Lent loaf
```
Model After Training: 
```
On a clear day, our community is growing and we are enjoying the beauty and tranquility of our natural surroundings. While it may not be a
```

### Training Loss and HellaSwag Evaluation

Here is a figure showing the training/validation loss and model's performance on the HellaSwag benchmark:

![Model's Performance](results/fig.PNG)


## Installation

### Install from PyPI

To install the package from PyPI, run the following command:

```bash
pip install GPT-from-scratch
```

### Install from Github

To install the latest version directly from GitHub, use:

```bash
pip install git+https://github.com/MatinKhajavi/GPT-from-scratch.git
```

## Usage



## License

This project is licensed under the MIT License.