name: training-hub description: Fine-tune LLMs using Red Hat training-hub library with SFT, LoRA, and OSFT algorithms. Use when preparing JSONL datasets, running training jobs, configuring hardware, scaling to clusters, evaluating models, or deploying with vLLM.

Training Hub

Red Hat's unified library for LLM post-training: SFT, LoRA, and OSFT (continual learning).

Quick Reference

Installation

pip install training-hub              # Basic
pip install training-hub[lora]        # LoRA with Unsloth (2x faster)
pip install training-hub[cuda] --no-build-isolation  # CUDA support

Get Started Fast

# Get optimal config for your hardware
python scripts/recommend_config.py \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --hardware rtx-5090

Data Format

Training data must be JSONL with message structure:

{"messages": [{"role": "user", "content": "Q"}, {"role": "assistant", "content": "A"}]}

Validate before training:

python scripts/validate_dataset.py ./training_data.jsonl

For data preparation details, see DATA-FORMATS.md.

Training Methods

Supervised Fine-Tuning (SFT)

Full-parameter fine-tuning. Requires significant VRAM.

from training_hub import sft

result = sft(
    model_path="Qwen/Qwen2.5-7B-Instruct",
    data_path="./training_data.jsonl",
    ckpt_output_dir="./checkpoints",
    num_epochs=3,
    effective_batch_size=8,
    learning_rate=2e-5,
    max_seq_len=2048,
    max_tokens_per_gpu=45000,
)

LoRA Fine-Tuning

Memory-efficient adaptation (up to 2x faster, 70% less VRAM):

from training_hub import lora_sft

result = lora_sft(
    model_path="Qwen/Qwen2.5-7B-Instruct",
    data_path="./training_data.jsonl",
    ckpt_output_dir="./outputs",
    lora_r=16,
    lora_alpha=32,
    num_epochs=3,
    learning_rate=2e-4,
)

QLoRA (4-bit): Add load_in_4bit=True for large models on limited VRAM.

OSFT (Continual Learning)

Adapt without catastrophic forgetting:

from training_hub import osft

result = osft(
    model_path="meta-llama/Llama-3.1-8B-Instruct",
    data_path="./domain_data.jsonl",
    ckpt_output_dir="./checkpoints",
    unfreeze_rank_ratio=0.25,
    effective_batch_size=16,
    learning_rate=2e-5,
)

For all parameters, see ALGORITHMS.md.

Hardware Support

# Check if your config fits
python scripts/estimate_memory.py \
  --model meta-llama/Llama-3.1-70B-Instruct \
  --method lora \
  --hardware h100 \
  --num-gpus 8

For hardware-specific configs, see HARDWARE.md.

Scaling

Multi-GPU:

result = sft(..., nproc_per_node=8)

Multi-node:

result = sft(..., nnodes=2, node_rank=0, nproc_per_node=8, rdzv_endpoint="0.0.0.0:29500")

For Slurm, Kubernetes, and datacenter deployments, see SCALE.md.

Algorithm Selection

Documentation

Utility Scripts

Troubleshooting

CUDA OOM: Reduce max_tokens_per_gpu, use LoRA + QLoRA, or add GPUs

Dataset errors: Run python scripts/validate_dataset.py first

LoRA multi-GPU: Requires torchrun --nproc-per-node=N script.py

Training diverges: Lower learning_rate (try 1e-5 for SFT, 1e-4 for LoRA)

For more, see TROUBLESHOOTING.md.