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name: llm-training description: Use when "training LLM", "finetuning", "RLHF", "distributed training", "DeepSpeed", "Accelerate", "PyTorch Lightning", "Ray Train", "TRL", "Unsloth", "LoRA training", "flash attention", "gradient checkpointing" version: 1.0.0

LLM Training

Frameworks and techniques for training and finetuning large language models.

Framework Comparison

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Accelerate (HuggingFace)

Minimal wrapper for distributed training. Run accelerate config for interactive setup.

Key concept: Wrap model, optimizer, dataloader with accelerator.prepare(), use accelerator.backward() for loss.

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DeepSpeed (Large Models)

Microsoft's optimization library for training massive models.

ZeRO Stages:

• Stage 1: Optimizer states partitioned across GPUs
• Stage 2: + Gradients partitioned
• Stage 3: + Parameters partitioned (for largest models, 100B+)

Key concept: Configure via JSON, higher stages = more memory savings but more communication overhead.

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TRL (RLHF/DPO)

HuggingFace library for reinforcement learning from human feedback.

Training types:

• SFT (Supervised Finetuning): Standard instruction tuning
• DPO (Direct Preference Optimization): Simpler than RLHF, uses preference pairs
• PPO: Classic RLHF with reward model

Key concept: DPO is often preferred over PPO - simpler, no reward model needed, just chosen/rejected response pairs.

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Unsloth (Fast LoRA)

Optimized LoRA finetuning - 2x faster, 60% less memory.

Key concept: Drop-in replacement for standard LoRA with automatic optimizations. Best for 7B-13B models.

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Memory Optimization Techniques

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Decision Guide

Resources

• Accelerate: https://huggingface.co/docs/accelerate
• DeepSpeed: https://www.deepspeed.ai/
• TRL: https://huggingface.co/docs/trl
• Unsloth: https://github.com/unslothai/unsloth