An Open Source Ecosystem for Large Language Models (LLMs)
GigaBase is a comprehensive open-source platform dedicated to advancing Large Language Model (LLM) research, development, and deployment. We provide a collaborative space for researchers, engineers, data scientists, and AI enthusiasts to share datasets, models, training scripts, evaluation benchmarks, and documentation.
Our mission is to democratize AI by creating an accessible, well-documented, and discoverable ecosystem where the global community can:
- Share and discover high-quality datasets
- Collaborate on transformer architectures and model innovations
- Exchange training methodologies and best practices
- Benchmark and evaluate model performance
- Deploy models efficiently at scale
- Learn and grow together through comprehensive documentation
GigaBase/
├── datasets/ # Curated datasets with documentation
├── models/ # Model architectures and implementations
├── training/ # Training scripts and pipelines
├── evaluation/ # Benchmarks and evaluation tools
├── deployment/ # Deployment and inference utilities
└── docs/ # Comprehensive documentation
Curated, cleaned, and well-documented datasets for LLM training and fine-tuning.
- Text corpora, code repositories, multilingual data
- Domain-specific datasets (medical, legal, scientific, etc.)
- Each dataset includes: source info, license, preprocessing steps, keywords/tags
Transformer architectures, model cards, and research implementations.
- Pre-trained models and checkpoints
- Novel architectures and optimizations
- Model cards with training details and performance metrics
Scripts, configurations, and utilities for model training and fine-tuning.
- Training pipelines and distributed training setups
- Fine-tuning scripts for specific tasks
- Hyperparameter configurations and best practices
Benchmarks, evaluation scripts, and performance metrics.
- Standard benchmark implementations
- Custom evaluation metrics
- Result comparisons and leaderboards
Tools and guides for deploying LLMs in production.
- Serving infrastructure and APIs
- Inference optimizations
- Docker containers and cloud deployment guides
Comprehensive guides, tutorials, and API documentation.
- Getting started guides
- Contribution guidelines
- Best practices and tutorials
- Browse the repository to find datasets, models, or tools
- Read the Getting Started Guide
- Contribute by following our Contribution Guidelines
- Engage with the community through issues and discussions
We welcome contributions from everyone! Here's how you can get involved:
- Fork this repository
- Choose an area to contribute (datasets, models, training, etc.)
- Create a new branch for your contribution
- Add your contribution with proper documentation (
.mdfiles) - Submit a pull request
See our Detailed Contribution Guide for more information.
All contributions should include:
- Keywords/Tags: Use
#LLM #transformer #AI #NLP #dataset #training #benchmarketc. - Clear Documentation: Every contribution needs a
.mdfile with description, usage, and tags - Metadata: Include license info, dependencies, and requirements
- Examples: Provide sample usage and code snippets
#LLM #transformer #AI #machinelearning #NLP #open-source #dataset
#training #benchmark #docs #python #deep-learning #research #contribution
#fine-tuning #inference #deployment #serving #evaluation #metrics
- Getting Started - New to GigaBase? Start here!
- Contributing Guide - How to contribute effectively
- Dataset Template - Template for dataset documentation
- Model Template - Template for model documentation
- Training Template - Template for training scripts
- Issues: Browse open issues tagged with
help wantedorgood first issue - Discussions: Join our community discussions
- Pull Requests: Review and contribute to open PRs
This project is licensed under the MIT License - see the LICENSE file for details.
Thanks to all contributors who help build this open-source LLM ecosystem!
Let's build the future of AI together! 🤖✨
For questions or support, please open an issue or start a discussion.