semvec/pypi-packages huggingface dataset:
ahp-gaussiano 0.1.2
Pacote para cálculo de AHP Gaussiano
Requires Python: >=3.6

# AHP Gaussiano

Este pacote implementa o mÃ©todo AHP Gaussiano para anÃ¡lise de decisÃµes multicritÃ©rio.

## InstalaÃ§Ã£o

```sh
pip install ahp_gaussiano
mixture-of-depth 1.2.0
Unofficial implementation for the paper "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models"
Requires Python: >=3.6

# Unofficial implementation for the paper "Mixture-of-Depths"


## Introduction
This is an unofficial implementation for the paper [Mixture-of-Depths: Dynamically allocating compute in transformer-based language models](https://arxiv.org/abs/2404.02258)

## Currently supported models

| Model  | Supported? |
| ------------- | ------------- |
| Mistral  |  ✅ |
| Mixtral  |  ✅ |
| LLama  |  ✅ |
| LLama2  |  ✅ |
| LLama3  |  ✅ |
| Gemma  |  ✅ |
| BLOOMZ  |  ✅ |
| BLOOM  |  ✅ |
| DeepSeek  |  ✅ |
| Phi (1.5 & 2)  |  ✅ |
| Qwen2  |  ✅ |
| StarCoder2  |  ✅ |
| Qwen2-MoE  |  ❓ |
| Solar  |  ❓ |
| Baichuan  |  ❌ |
| ChatGLM3  |  ❌ |
| InternLM  |  ❌ |
| Olmo  |  ❌ |
| XVERSE  |  ❌ |
| Yi  |  ❌ |
| Yuan  |  ❌ |

## 💾 Installation
```bash
pip install mixture-of-depth
```
Both **Linux**, **Windows** and **MacOS** are supported.
## 🏁 Quick Start

### High-level API (tranformers-compatible)
```python
from transformers import AutoModelForCausalLM
from MoD import apply_mod_to_hf

# Initialize your model from an available hf model
model= AutoModelForCausalLM.from_pretrained("some-repo/some-model")
# Convert the model to include the mixture of depths layers
model = apply_mod_to_hf(model)
# train the model
# ...
# save the model
model.save_pretrained('some_local_directory')
```
### Loading the converted Model
To utilize the converted model, you will need to load the model from the AutoClass. Below is an example demonstrating how to load the model from a local directory:
```python
from MoD import AutoMoDModelForCausalLM

# Replace 'path_to_your_model' with the actual path to your model's directory
model = AutoMoDModelForCausalLM.from_pretrained('path_to_your_model')
```
### Using generate()
Before calling the hf generate() method please explicitly use eval() on the model

## 🫱🏼‍🫲🏽 Contributing
We welcome contributions from the community, whether it's adding new features, improving documentation, or reporting bugs. Please refer to our contribution guidelines before making a pull request.

## 📜 License
This repo is open-sourced under the Apache-2.0 license.

## Citation
If you use our code in your research, please cite it using the following Bibtex entry:

```bibtex
@article{MoD2024,
  title={Unofficial implementation for the paper "Mixture-of-Depths"},
  author={AstraMind AI},
  journal={https://github.com/astramind-ai/Mixture-of-depths},
  year={2024}
}
```
## Support
For questions, issues, or support, please open an issue on our GitHub repository.
loducode-web3 0.1.15
Basic components for the development of loducode s.a.s. for web3
Keywords: web3, loducode_web3, loducode

# Loducode web3

Basic components for the development of loducode s.a.s.

### install

`pip install loducode_web3==0.1.15`

## functionalities

- **Models**
    - AttributeNft
    - Nft

## Commands

- python setup.py sdist bdist_wheel
- twine upload --repository pypi dist/loducode_web3-0.1.15*

## push
git tag -a 0.1.15 -m "annotation for this release"

entrar a la carpeta loducode_web3 y correr
- django-admin makemessages
- django-admin compilemessages

#### Version 0.1.14
- uodate method to_wei
- uodate method to_hex

#### Version 0.1.14
- uodate method build_transaction

#### Version 0.1.13
- update abi with json

#### Version 0.1.12
- default value in call methods with ""

#### Version 0.1.8
- delete attributes in model Nft it is not necessary

#### Version 0.1.7
- add enable_unaudited_hdwallet_features

#### Version 0.1.6
- update requirements loducode utils and web3
- add method create wallet

#### Version 0.1.5
- update requirements loducode utils

#### Version 0.1.4
- update requirements loducode utils

#### Version 0.1.4
- delete requirement sycopg2-binary==2.8.4

#### Version 0.1.3
- add translation

#### Version 0.1.2
- add method add_white_list

#### Version 0.1.1
- Models abstracts

#### Version 0.1.0
- Models abstracts

#### Version 0.0.1
- first version

#### Version 0.0.2
- bug imports

#### Version 0.0.1
- first version
randomchessgame 3.0.0
Python library to come in handy when random chess game is needed.
Requires Python: >=3.7

# randomchessgame

Python library to come in handy when random chess game is needed.

# Installation.

     $ pip install randomchessgame

# Basic usage.

     from randomchessgame import random_chess_game
     random_chess_game()

output will look like this:

     1. Nf3 Nf6 2. g3 g6 3. Bg2 Bg7 4. c4 O-O 5. O-O d5 6. cxd5 Nxd5 7. Nc3 Nb6 8. d3 Nc6

# Usage with parameters.

You can specify the minimum length, exact length, first move etc. To do it, you have to specify it as parameter while calling a function. Below are listed all possible parameters with their brief explainations.

### length 

Returns random game with exact length as specified by user while calling a function. If length is set to 10, the game will have 10 moves (e4 e5 count as two separate moves) 

     random_chess_game(length=10)
     1. d4 c5 2. Nf3 cxd4 3. Bf4 Nf6 4. e3 Nd5 5. exd4 Nxf4

### min_length

Returns random game with minimum length as specified by user while calling a function. If min_length is set to 10, the game will have more moves than 10. May be 11 and 111 as well but never less or equal to min_length value.

     random_chess_game(min_length=10)
     1. d4 d5 2. Nf3 Nf6 3. c4 c6 4. Nc3 e6 5. Bg5 h6 6. Bxf6 Qxf6 7. e3 Nd7 8. Rc1 g6 9. Be2 Qe7

### startswith

Returns random game that has started with a specific move. There is no need to pass move characters (1. e4 e5 2. Nf3 Nf6 3. etc), only moves ('e4 e5 Nf3 Nf6).

     random_chess_game(startswith='e4 e5 Nf3 Nf6')
     1. e4 e5 2. Nf3 Nf6 3. Bc4 Nxe4 4. Nc3 Nxc3 5. dxc3 f6 6. Nh4 g6 7. f4 f5 8. Nxf5 d5

### endswith_mate

Returns random game that has ended with a mate.

     random_chess_game(endswith_mate=True)
     ..... 75. a7 Rb8 76. Rxb8 Kg6 77. a8=Q Kg5 78. Qe4 h5 79. Rg3+ Kh6 80. Qg6#


### endswith_move

Returns random game that has ended with a specific move.

     random_chess_game(endswith_move='Kf4')
     ..... 45. Kf4 e5+ 46. dxe5+ Ke6 47. Ke4 h5 48. gxh5 gxh5 49. c5 h4 50. Kf4

### cleargame

Returns random game without movechars.

     random_chess_game(cleargame=True)
     Nf3 h6 g3 d6 Bg2 Nd7 O-O g5 d4 Bg7 c4 Nf8 Nc3 Ng6 e4 g4 Ne1 e5 

### adding your own file

There is a possibility to add your own file with games and make the func pick a game from it. To do so, pass path to the file while calling a function. Remember that the file should not contain pgn tags ('0-1' , '1-0', '1/2-1/2'), opening name, comments nor clock.

     random_chess_game(file='path/to/file/with/games:)

### mixing params

You can mix parameters to get wanted result. It can look like this:

     random_chess_game(endswith_mate=True, startswith='e4', min_length=25, cleargame=True)
     e4 g6 d4 Bg7 Nc3 d6 h4 Nf6 f3 h5 Be3 c6 Qd2 b5 Bd3 Qc7 O-O-O a6 e5 Nd5 
     

In case of file not containing accurate game, it will return:

     No games found with the given criteria.