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LLM Fine-tuning

Customize large language models for specific domains and use cases through advanced fine-tuning techniques and optimization strategies.

Category:AI & Machine Learning
Level:Advanced
Duration:6-8 weeks
#llm#fine-tuning#machine-learning#optimization

Overview

LLM fine-tuning involves adapting pre-trained language models to perform better on specific tasks or domains. This includes techniques like LoRA, QLoRA, and full parameter fine-tuning.

Learning Path

1

Understanding transformer architectures and pre-training

2

Learning different fine-tuning approaches (full, LoRA, QLoRA)

3

Data preparation and dataset curation techniques

4

Training optimization and hyperparameter tuning

5

Model evaluation and deployment strategies

Recommended Tools

Hugging Face Transformers
PyTorch/TensorFlow
LoRA/QLoRA
Weights & Biases
CUDA/GPU Computing
Docker/Kubernetes

Prerequisites

  • Strong machine learning background
  • Experience with deep learning frameworks
  • Understanding of transformer architectures

Skill Info

Added

January 17, 2024

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Learners

2620+

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