v0.3.0 - Granite 4, Mamba, Env var support, and Memory Estimation

This release introduces memory profiling capabilities, enhanced distributed training orchestration, and support for Granite 4 and Mamba models. Backend implementations have been updated to instructlab-training v0.12.1 and mini-trainer v0.3.0.

What's New

Memory Profiling API (Experimental)

• New memory estimation tool for fine-tuning workloads
• Reports per-GPU VRAM requirements (parameters, optimizer state, gradients, activations, outputs)
• Supports both SFT and OSFT algorithms
• Returns low/expected/high memory bounds for better resource planning
• Includes Liger-kernel-aware adjustments
• Example notebook and documentation included

Enhanced Distributed Training

• Automatic torchrun configuration from environment variables
• Full compatibility with Kubeflow and other orchestration systems
• Support for auto and gpu process count specifications
• Centralized launch parameter handling with hierarchical priority
• Improved validation with clear conflict warnings and error messages
• Flexible argument types (string or integer) for multi-node parameters
• Explicit master address and port configuration options

Model Support Expansion

• Granite 4 support (transformers>=4.57.0)
• Mamba model support with optional CUDA acceleration (mamba-ssm[causal-conv1d]>=2.2.5)
• Enhanced compatibility through dependency updates

Infrastructure Improvements

• Uncapped NumPy for better forward compatibility
• Minimum Numba version raised to 0.62.0
• Liger kernel pinned to >=0.5.10 for stability
• Updated backend implementations (instructlab-training>=0.12.1, rhai-innovation-mini-trainer>=0.3.0)

What's Changed

• Pinning liger-kernal version by @Fiona-Waters in #9
• Adding min dependencies for Granite 4 / Mamba support by @Maxusmusti in #14
• uncap numpy and raise minimum numba version by @RobotSail in #15
• Adding basic API for memory profiling (src/training_hub/profiling) by @mazam-lab in #11
• feat(traininghub): Use torchrun environment variables for default configuration by @szaher in #13
• Update backend implementation dep versions in pyproject.toml by @Maxusmusti in #19

New Contributors

• @Fiona-Waters made their first contribution in #9
• @mazam-lab made their first contribution in #11
• @szaher made their first contribution in #13

Full Changelog: v0.2.0...v0.3.0