Can I fine-tune a 7B model on an 8GB GPU?

Yes, QLoRA makes it possible to fine-tune a 7B parameter model on approximately 6GB-8GB of VRAM, though it may require tight sequence length limits.

Does 4-bit quantization hurt the model's accuracy?

No, using the 4-bit NormalFloat (NF4) data type introduced in QLoRA has been shown to match the performance of full 16-bit fine-tuning.

What are Paged Optimizers?

Paged Optimizers use NVIDIA unified memory to offload optimizer states to CPU RAM during memory spikes, preventing Out-of-Memory (OOM) errors during training.

What is the primary advantage of QLoRA over standard LoRA?

QLoRA reduces memory usage significantly by quantizing the base model to 4-bit precision, allowing much larger models to fit on smaller GPUs without losing performance.

How do I deploy a QLoRA trained model?

You can either load the quantized base model with the saved adapters for inference, or merge the adapters into a full-precision model for standard deployment.

QLoRA Fine-Tuning

Name: QLoRA Fine-Tuning
Author: itsmostafa

byitsmostafa

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데이터 과학 및 ML

Enables memory-efficient fine-tuning of large language models using 4-bit quantization and LoRA adapters.

소개

This skill provides specialized guidance for implementing QLoRA (Quantized Low-Rank Adaptation) to fine-tune massive language models on consumer-grade hardware. By leveraging 4-bit NormalFloat (NF4) quantization, double quantization, and paged optimizers, it allows engineers to train models with up to 65B parameters on a single 48GB GPU. It serves as an advanced extension of the LoRA skill, focusing on extreme VRAM optimization while maintaining performance parity with 16-bit fine-tuning methods.

주요 기능

10 GitHub stars
Advanced troubleshooting for CUDA, dtypes, and OOM errors
Comprehensive VRAM requirement mapping for 7B to 70B models
Implementation patterns for 4-bit NormalFloat (NF4) quantization
Production-ready training scripts using Transformers, PEFT, and TRL
Memory-saving techniques including Double Quantization and Paged Optimizers

사용 사례

Fine-tuning a 70B parameter model on a single 48GB GPU
Optimizing cloud training costs by reducing hardware requirements
Running LLM adaptation tasks on consumer GPUs with limited VRAM (8GB-24GB)

소개

주요 기능

10 GitHub stars
Advanced troubleshooting for CUDA, dtypes, and OOM errors
Comprehensive VRAM requirement mapping for 7B to 70B models
Implementation patterns for 4-bit NormalFloat (NF4) quantization
Production-ready training scripts using Transformers, PEFT, and TRL
Memory-saving techniques including Double Quantization and Paged Optimizers

사용 사례

Fine-tuning a 70B parameter model on a single 48GB GPU
Optimizing cloud training costs by reducing hardware requirements
Running LLM adaptation tasks on consumer GPUs with limited VRAM (8GB-24GB)