Galvatron is a multi-modal conversational model that leverages the power of MPT-30B, and multi-modal adapters to process a variety of input modalities. Built on a robust framework, Galvatron allows you to easily integrate language models with other multi-modal adapters and fine-tune them on multi-modal instruction tuning datasets.
- Uses MPT30b, a highly capable language model developed by MosaicML.
- Supports Multi-Modal input processing with various adapters.
- Aims to facilitate more human-like conversations with AI.
You can install Galvatron by two methods - using pip, or by cloning the repository from GitHub.
You can install Galvatron using pip as follows:
pip install galvatronTo clone the Galvatron repository from GitHub, use the following commands:
git clone https://github.com/kyegomez/Galvatron.git
cd Galvatron
pip install -e .To get started with Galvatron, please refer to the example given below:
from galvatron import GalvatronBaseLM
galvatron = GalvatronBaseLM(use_4bit_quantization=True)
text = "What is your theory of everything?"
response = galvatron.generate(text)
print(response)Our current plan for Galvatron's development includes the following:
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Integrate the Language Model with other Multi-modal Adapters: We aim to enhance the versatility of Galvatron by allowing it to process different input modalities effectively.
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Fine-tune Galvatron on Multi-modal Instruction Tuning Datasets: To improve its performance and applicability, we plan to fine-tune Galvatron on multi-modal instruction tuning datasets.
We welcome community contributions towards achieving these goals. If you have any suggestions or ideas, please feel free to create an issue or a pull request.
We love contributions! If you want to contribute to Galvatron, please fork the repository and create a pull request. If you find any issues or have any feature requests, feel free to create an issue.
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Galvatron is open-source software, licensed under MIT.