Metadata-Version: 2.1
Name: ttl-torch
Version: 0.0.4
Summary: Paper - Pytorch
Home-page: https://github.com/kyegomez/TTL
License: MIT
Keywords: artificial intelligence,deep learning,optimizers,Prompt Engineering
Author: Kye Gomez
Author-email: kye@apac.ai
Requires-Python: >=3.10,<4.0
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: loguru
Requires-Dist: torch
Requires-Dist: zetascale
Project-URL: Documentation, https://github.com/kyegomez/TTL
Project-URL: Repository, https://github.com/kyegomez/TTL
Description-Content-Type: text/markdown

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## TTL
Pytorch Implementation of the paper: "Learning to (Learn at Test Time): RNNs with Expressive Hidden States"




## Install
```bash
$ pip install ttl-torch

```

## Usage
```python

import torch
from ttl_torch.ttl_linear import TTLinear


input_dim, output_dim = 10, 10  # Dimensions for the linear model
ttt_layer = TTLinear(input_dim, output_dim)

# Generate some example data
example_data = [
    torch.randn(1, input_dim, output_dim) for _ in range(5)
]

# Forward pass through the TTT layer
output_data = ttt_layer(example_data)

for i, output in enumerate(output_data):
    print(f"Output at step {i}: {output}")

```


# License
MIT


## Citation
```bibtex
@misc{sun2024learninglearntesttime,
    title={Learning to (Learn at Test Time): RNNs with Expressive Hidden States}, 
    author={Yu Sun and Xinhao Li and Karan Dalal and Jiarui Xu and Arjun Vikram and Genghan Zhang and Yann Dubois and Xinlei Chen and Xiaolong Wang and Sanmi Koyejo and Tatsunori Hashimoto and Carlos Guestrin},
    year={2024},
    eprint={2407.04620},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    url={https://arxiv.org/abs/2407.04620}, 
}

```
