semvec/pypi-packages huggingface dataset:
simplified-transormer-torch 0.0.1
Paper - Pytorch
License: MIT
Requires Python: >=3.9,<4.0
Keywords: artificial intelligence,deep learning,optimizers,Prompt Engineering

[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)

# SimplifiedTransformers
The author presents an implementation for Simplifying Transformer Blocks. The standard transformer blocks are complex and can lead to architecture instability. In this work, the author investigates how the standard transformer block can be simplified. Through signal propagation theory and empirical observations, the author proposes modifications that remove several components without sacrificing training speed or performance. The simplified transformers achieve the same training speed and performance as standard transformers, while being 15% faster in training throughput and using 15% fewer parameters.


# Install
```


```

--------

## Usage
```python

import torch
from simplified_transformers.main import SimplifiedTransformers

model = SimplifiedTransformers(
    dim=4096,
    depth=6,
    heads=8,
    num_tokens=20000,
)

x = torch.randint(0, 20000, (1, 4096))

out = model(x)
print(out.shape)

```






# License
MIT
anyapi2 0.0.6
Boilerplate code for api integrations
License: MIT
Keywords: requests api

# AnyAPI

AnyAPI is a small library to get rid of boilerplate code reused in many projects where interaction with APIs is needed.

AnyAPI:
* uses `requests.Session`
* has default timeout
* has configurable pool size
* has retry policy in case of 429 error
* has `get` and `post` methods defined

## How to use

```sh
pip install anyapi2
```

```python
from anyapi import API


class SomeServiceAPI(API):
    BASE_URL = 'https://some-api-service.com'


some_service = SomeServiceAPI()
response = some_service.get('/v1/items')
response.raise_for_status()
result = response.json()
```