DeepSlyme is a powerful and highly customizable LLM training framework built natively on Slyme. It is designed to help researchers and developers seamlessly orchestrate distributed training and accelerate model optimization and iteration.
The Trainer class in traditional deep learning frameworks is often a highly encapsulated "black box", filled with intricate object nesting internally. DeepSlyme abandons this paradigm, turning instead to the node-based and functional context features of the Slyme framework, presenting you with an extremely clear model training pipeline.
DeepSlyme requires Python 3.9+. You can install it directly via pip:
pip install deepslyme(Note: If you are new to the Slyme series, it is highly recommended that you first read the official Slyme documentation.)
Transparent Execution Flow: Break free from the black box constraints of traditional Trainers. DeepSlyme eliminates complex object nesting and endless code jumping. Data loading, model forward passes, gradient backpropagation, and parameter updates—every step is clearly visible, giving you absolute control over the entire training loop.
High Extensibility: Focus your energy on the algorithm itself rather than being constrained by tedious boilerplate code. DeepSlyme adopts a completely decoupled architecture, making the process of testing new ideas significantly faster and smoother.
Universal Composability: Thanks to the unified ecosystem of the underlying Slyme framework, you can seamlessly extract and reuse custom Nodes across different projects. Meanwhile, you can easily integrate and call out-of-the-box modules from the entire ecosystem, merging your breakthrough research into the broader community.
To dive deeper into DeepSlyme's architecture and usage, please refer to our official documentation site.
👉 Read the Official DeepSlyme Documentation
This project is licensed under the Apache-2.0 License.