Metadata-Version: 2.1
Name: palme
Version: 0.0.5
Summary: palme - Pytorch
Home-page: https://github.com/kyegomez/palme
License: MIT
Keywords: artificial intelligence,attention mechanism,transformers
Author: Kye Gomez
Author-email: kye@apac.ai
Requires-Python: >=3.6,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: SentencePiece
Requires-Dist: accelerate
Requires-Dist: datasets
Requires-Dist: deepspeed
Requires-Dist: einops
Requires-Dist: lion-pytorch
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: torch
Requires-Dist: transformers
Description-Content-Type: text/markdown

# 🌴 PALM-E: A Multi-Modal AI Model 

This is the open source implementation of the SOTA multi-modality foundation model "PaLM-E: An Embodied Multimodal Language Model" from Google, PALM-E is a single large embodied multimodal model, that can address a variety of embodied reasoning tasks, from a variety of observation modalities, on multiple embodiments, and further, exhibits positive transfer: the model benefits from diverse joint training across internet-scale language, vision, and visual-language domains.


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## 🚀 Quick Start

### Installation 📦

```sh
pip install palme
```

### Usage 🎨

```python
import torch
from palme import PalmE

#usage
img = torch.randn(1, 3, 256, 256)
caption_tokens = torch.randint(0, 4)

model = PalmE()
output = model(img, caption_tokens)

```
---

## Contribute || Be Part of the PALM-E Adventure 🤝

Your brilliance is needed! Join us, and together, let's make PALM-E even more awe-inspiring:

1. **Get Your Copy**: Fork the PALM-E repo.
2. **Make It Local**: Clone your fork.
3. **Prep Your Tools**: Install the necessities.
4. **Discover & Innovate**: Dive into the code.
5. **Craft Your Magic**: Branch and code away.
6. **Show & Tell**: Push your changes and craft a pull request.

🐞 Fixes, 🎨 enhancements, 📝 docs, or 💡 ideas – all are welcome! Let's shape the future of AI, hand in hand.

## Roadmap

- 🕵️ Verify decoder configurations.
- 🚂 Recreate the training strategy detailed in the paper.
- 🌐 Train on the datasets used in the paper.

## 📘 Documentation
* Documentation will come soon


