Which framework is best for fast LLM finetuning?

For 7B-13B models, Unsloth is the recommended choice as it is up to 2x faster and uses 60% less memory than standard methods.

What is the benefit of using DeepSpeed ZeRO stages?

ZeRO stages allow you to train massive models by partitioning optimizer states, gradients, and parameters across multiple GPUs, significantly reducing per-GPU memory requirements.

How do I reduce VRAM usage during LLM training?

You can use memory optimization techniques such as 4-bit quantization (QLoRA), gradient checkpointing, mixed precision (bf16), and Flash Attention.

Why should I choose DPO over PPO for model alignment?

Direct Preference Optimization (DPO) is generally preferred because it is simpler to implement, more stable, and does not require fitting a separate reward model.

LLM Training & Finetuning

Name: LLM Training & Finetuning
Author: eyadsibai

byeyadsibai

Data Science & ML

Streamlines the process of training and finetuning large language models using industry-standard frameworks and memory optimization techniques.

About

This skill provides specialized guidance for the end-to-end LLM training lifecycle, ranging from simple LoRA finetuning to massive-scale distributed training. It helps developers navigate complex frameworks like DeepSpeed, Accelerate, and TRL while implementing critical memory-saving optimizations such as QLoRA, Flash Attention, and gradient checkpointing. Whether you are performing instruction tuning with SFT or aligning models via DPO, this skill ensures you select the right architecture and configuration for your specific hardware and model size.

Key Features

Step-by-step guidance for RLHF and Direct Preference Optimization (DPO)
0 GitHub stars
Memory optimization strategies like QLoRA, Flash Attention, and gradient checkpointing
Expert decision guides for selecting training tools based on model parameter count
Comprehensive framework comparisons including DeepSpeed, Accelerate, and Unsloth
Distributed training configurations for multi-GPU and multi-node clusters

Use Cases

Transitioning base models to instruction-tuned variants using Supervised Finetuning (SFT)
Implementing efficient parameter-efficient finetuning (PEFT) on consumer-grade hardware
Scaling training for 70B+ parameter models using DeepSpeed ZeRO-3 partitioning

About

Key Features

Step-by-step guidance for RLHF and Direct Preference Optimization (DPO)
0 GitHub stars
Memory optimization strategies like QLoRA, Flash Attention, and gradient checkpointing
Expert decision guides for selecting training tools based on model parameter count
Comprehensive framework comparisons including DeepSpeed, Accelerate, and Unsloth
Distributed training configurations for multi-GPU and multi-node clusters

Use Cases

Transitioning base models to instruction-tuned variants using Supervised Finetuning (SFT)
Implementing efficient parameter-efficient finetuning (PEFT) on consumer-grade hardware
Scaling training for 70B+ parameter models using DeepSpeed ZeRO-3 partitioning