⚠ NO TOKEN HAS LAUNCHED YET! Please be cautious of any unofficial token claims.
⚠ NO TOKEN HAS LAUNCHED YET! Please be cautious of any unofficial token claims.
⚠ NO TOKEN HAS LAUNCHED YET! Please be cautious of any unofficial token claims.
Qryptum is a next-generation machine learning framework designed for efficiency and high-performance computation. Built on the cutting-edge Mojo programming language, Qryptum aims to redefine AI development by leveraging static graph compilation and low-level optimizations, achieving speeds comparable to or surpassing well-established frameworks.
Mojo, developed by Modular, is engineered to provide the ease of Python while delivering performance similar to Rust or C++. Qryptum capitalizes on this by integrating custom Tensor and TensorShape implementations, optimizing execution performance, and streamlining AI model training and inference.
While Qryptum is still in its early development phase, continuous improvements and upgrades are being made to push the boundaries of AI computation. Stay tuned for upcoming benchmarks and feature enhancements!
To run Qryptum benchmarks, execute the following:
mojo -I . examples/housing.mojo
mojo -I . examples/sin_estimate.mojo
mojo -I . examples/mnist.mojoFor comparison, a PyTorch-based implementation can be run using:
pip install -r python-requirements.txt
python examples/housing.py
python examples/sin_estimate.py
python examples/mnist.py- Optimized matrix multiplication and convolution kernels
- Custom Tensor and TensorShape implementation
- Enhanced benchmarking and model execution performance
- Profiling tools and additional performance testing
- Additional operators: Slice, (Un)Squeeze, Concat, Clip, Gather, Split, FMA, etc.
- Expanded layer support and activation functions
- Graph submodules & graph concatenation
- Computer vision benchmarks
- Advanced parallelization techniques
- GPU acceleration support
- Improved data loaders for training efficiency
- Automatic tuning and performance optimizations
- ONNX and MAX compatibility
Qryptum is integrating blockchain technology to power AI computation. A dedicated Qryptum Token (QRYPT) will be introduced to facilitate seamless and decentralized access to machine learning resources. This token will be used directly for:
- Payment of compute power
- Accessing LLM (Large Language Model) resources
- Incentivizing contributions to the Qryptum ecosystem
⚠ NO TOKEN HAS LAUNCHED YET! Please be cautious of any unofficial token claims.
Qryptum is a community-driven project, and contributions are highly encouraged! Whether it's bug fixes, performance enhancements, or new feature proposals, your input helps improve the ecosystem.
- Fork the project
- Create a feature branch
- Commit your changes
- Push to the branch
- Open a Pull Request
Before submitting a PR, ensure that all unit tests pass using:
mojo run -I . test/test_ops.mojoIf introducing a significant feature, please include new test cases to validate its functionality.
Distributed under the Apache 2.0 License with LLVM Exceptions. See LICENSE and the LLVM License for more information