semvec/pypi-packages huggingface dataset:
vibeAI23 1.0.2
A library for predicting and blocking suspicious traffic
Requires Python: >=3.6
Keywords: cyberAI,AIcyber,vibeAI23

# Secure Application With AI

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)                 

[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)   [![Python 3.8](https://img.shields.io/badge/python-3.8-yellow.svg)](https://www.python.org/downloads/release/python-380/)   


## Functionality of the vibeAI2

- Better Looking With AI  
- Block unauthorized  user 
- Authorized user can Allow

## Usage

- Make sure you have Python installed in your system.
- Run Following command in the CMD.
 ```
  pip install vibeAI2
  ```
## Example

 ```
# test.py
from vibeAI2 import data_breache, predict_breach

  ```

## Run the following Script.
 ```
  python test.py
 ```



## Note 
- I have tried to implement all the functionality, it might have some bugs also. Ignore that or please try to solve that bug.
geov 0.0.2
The GeoV model is a large langauge model designed by Georges Harik and uses Rotary Positional Embeddings with Relative distances (RoPER). We have shared a pretrained 9B parameter model.
Keywords: machine learning

# GeoV

## Overview

The GeoV model was designed by Georges Harik and uses 
[Rotary Positional Embeddings with Relative distances (RoPER)](http://research.labml.ai/RoPER.html) 
by [Georges Hark](https://twitter.com/ghark) and [Varuna Jayasiri](https://twitter.com/vpj).

[RoPER](http://research.labml.ai/RoPER.html), in addition to using relative positions in the attention score
calculation by RoPE embeddings, adds relative positional information explicitly to value embeddings. 
Specifically, it incorporates the relative positions of the tokens paid attention to.
RoPER has given better performance in some algorithmic tasks, and seems comparable to RoPE in language modeling.

The GeoV tokenizer uses [SentencePiece](https://github.com/google/sentencepiece)
[unigram language model](https://arxiv.org/abs/1804.10959) and tokenizes symbols,
digits and new line characters separately, in order to achieve better performance on mathematical content and code.

This model was contributed by [gharik](https://huggingface.co/gharik) and [vpj](https://huggingface.co/vpj).

We have shared 9B parameter pre-trained model at [GeoV/GeoV-9b](https://huggingface.co/GeoV/GeoV-9b),
We plan to release checkpoints around every 20b tokens trained from here until around 300b tokens.
We will also train smaller and larger versions.
Our aim is to have broadly available smaller and larger models.

This implementation is built on top of [transformers](https://github.com/huggingface/transformers) library.

## Installations

```shell
pip install geov
```

## Generation

```python
from geov import GeoVForCausalLM, GeoVTokenizer

model = GeoVForCausalLM.from_pretrained("GeoV/GeoV-9b")
tokenizer = GeoVTokenizer.from_pretrained("GeoV/GeoV-9b")

prompt = "In mathematics, topology is the study of"

input_ids = tokenizer(prompt, return_tensors="pt").input_ids

gen_tokens = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.9,
    max_length=100,
)
gen_text = tokenizer.batch_decode(gen_tokens)[0]
```
donlabtools 0.22
donato lab [ca] imaging tools

# manifolds project @ Donato lab

### Installation

Recommended steps


0.  Open a command-line interface preferably inside the conda enviroment on your operating system (Windows, Mac)

1.  Use command-line to make a conda enviorment for running manifolds project:

conda create -n manifolds python=3.8

2.  Activate environment:

conda activate manifolds

3. Install dependencies (might have to do them 1 at a time; eventually will have a script for this)

pip install: matplotlib, os, numpy, scipy, tqdm, sklearn, pickle, parmap, networkx, pandas, cv2

4.  Install jupyter notebook 

conda install -c anaconda jupyter

5. Download and unzip the Donatolab binarization repo (https://github.com/donatolab/manifolds, click onthe green "Code" button)

6. Start jupyer notebook by typing it in at the command line

jupyter notebook
 
7. Navigate to the folder where the code unzipped and click on this file to start the jupyter notebook:

"Binarize_Suite2p_Inscopix.ipynb" 

8.  Run the first cell and then input the location of your suite2p folder in 2nd cell. Run the rest of the notebook.

9.  The code will save 2 files: binarized_traces.npz (a python numpy file) and binarized_traces.mat (a matlab file).

10(Optional) You can then use the last cell to visualize the traces and binarized versions for any specific cell.