training_hub

An algorithm-focused interface for common llm training, continual learning, and reinforcement learning techniques.

Support Matrix

Algorithm	InstructLab-Training	PEFT	VERL	Status
Supervised Fine-tuning (SFT)	✅	-	-	Implemented
Continual Learning (OSFT)	🔄	🔄	-	Planned
Direct Preference Optimization (DPO)	-	-	🔄	Planned
Low-Rank Adaptation (LoRA)	🔄	🔄	-	Planned
Group Relative Policy Optimization (GRPO)	-	-	🔄	Planned

Legend:

• ✅ Implemented and tested
• 🔄 Planned for future implementation
• - Not applicable or not planned

Implemented Algorithms

Supervised Fine-tuning (SFT)

Fine-tune language models on supervised datasets with support for:

• Single-node and multi-node distributed training
• Configurable training parameters (epochs, batch size, learning rate, etc.)
• InstructLab-Training backend integration

from training_hub import sft

result = sft(
    model_path="/path/to/model",
    data_path="/path/to/data",
    ckpt_output_dir="/path/to/checkpoints",
    num_epochs=3,
    learning_rate=1e-5
)

Installation

Basic Installation

pip install training-hub

Development Installation

git clone https://github.com/Red-Hat-AI-Innovation-Team/training_hub
cd training_hub
pip install -e .

CUDA Support

For GPU training with CUDA support:

pip install training-hub[cuda]
# or for development
pip install -e .[cuda]

Note: If you encounter build issues with flash-attn, install torch first:

pip install torch
pip install training-hub[cuda]

Getting Started