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PEFT source code LoRA, pre-fix tuning,..

Llama - LoRA fine-tuning code

Create PEFT - LoRA Model Seq2Seq

Trainable parameters of PEFT - LoRA model

get_peft_model

PEFT - LoRA - 8bit model of OPT 6.7B LLM

load_in_8bit

INT8 Quantization explained

Fine-tune a quantized model

bfloat16 and XLA compiler PyTorch 2.0

Freeze all pre-trained layer weight tensors

Adapter-tuning of PEFT - LoRA model

Save tuned PEFT - LoRA Adapter weights

Run inference of new PEFT - LoRA adapter - tuned LLM

Load your Adapter-tuned PEFT - LoRA model

Description:

Learn to optimize large language model fine-tuning for minimal GPU memory usage through a comprehensive 35-minute code tutorial demonstrating Parameter-Efficient Fine-tuning (PEFT) techniques. Master the implementation of Low-rank adaptation (LoRA) for adapter-tuning with INT8 quantized models using PyTorch 2.0, specifically designed for consumer GPUs with less than 80GB memory. Follow along with practical code demonstrations covering PEFT source code, Llama-LoRA fine-tuning, model creation, configuration, and training parameters. Explore advanced concepts including INT8 quantization, bfloat16 optimization, XLA compiler integration, and weight tensor freezing. Gain hands-on experience with adapter-tuning implementation, saving PEFT-LoRA weights, and running inference on newly tuned models, complete with access to a Jupyter notebook and supplementary HuggingFace blog resources.

Optimizing LLM Fine-Tuning with PEFT and LoRA Adapter-Tuning for GPU Performance

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#Computer Science #Machine Learning #Parameter-Efficient Fine-Tuning #PEFT #Deep Learning #PyTorch #Stable Diffusion #LoRA (Low-Rank Adaptation) #High Performance Computing #Parallel Computing #GPU Computing #Hugging Face

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