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LLM Fine Tuning Crash Course: 1 Hour End-to-End Guide
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Welcome to my comprehensive tutorial on fine-tuning Large Language Models (LLMs)! In this 1-hour crash course, I dive deep into the essentials and advanced techniques of LLM fine-tuning. This video is your gateway to understanding and applying cutting-edge methods like LoRA, QLoRA, PEFT, and more in your LLM projects.
🔍 What You'll Learn:
LoRA - Low-Rank Adaptation: Discover how LoRA revolutionizes parameter-efficient tuning and how to select the optimal settings for custom LLM training.
QLoRA - Quantized Low-Rank Adaptation: Understand the nuances of QLoRA for memory-efficient fine-tuning.
PEFT - Parameter-Efficient Fine-Tuning: Explore the transformative approach of PEFT, its pros and cons, and how it optimizes LLMs for specific tasks.
GPU Selection for Fine-Tuning: Get practical tips on choosing the right GPU for your project, with RunPod as an example.
Axolotl Tool Overview: Learn how Axolotl simplifies the fine-tuning process, supporting a range of models and configurations.
Hyperparameter Optimization: Gain insights into tweaking hyperparameters for optimal performance.
👨💻 Features of Axolotl:
Train models like llama, pythia, falcon, mpt.
Supports techniques including fullfinetune, lora, qlora, relora, and gptq.
Customize via YAML or CLI, handle various datasets, and integrate advanced features like xformer and multipacking.
Utilize single or multiple GPUs with FSDP or Deepspeed.
Log results to wandb, and more.
Whether you're a beginner or an experienced AI practitioner, this video equips you with practical knowledge and skills to fine-tune LLMs effectively. I'll guide you through each step, ensuring you grasp both the theory and application of these techniques.
👍 If you find this video helpful, please don't forget to LIKE and COMMENT! Your feedback is invaluable, and it helps me create more content tailored to your learning needs.
🔔 SUBSCRIBE for more tutorials on Gen AI, machine learning, and beyond. Stay tuned for more insights and tools to enhance your AI journey!
Join this channel to get access to perks:
#llm #generativeai #ai
🔍 What You'll Learn:
LoRA - Low-Rank Adaptation: Discover how LoRA revolutionizes parameter-efficient tuning and how to select the optimal settings for custom LLM training.
QLoRA - Quantized Low-Rank Adaptation: Understand the nuances of QLoRA for memory-efficient fine-tuning.
PEFT - Parameter-Efficient Fine-Tuning: Explore the transformative approach of PEFT, its pros and cons, and how it optimizes LLMs for specific tasks.
GPU Selection for Fine-Tuning: Get practical tips on choosing the right GPU for your project, with RunPod as an example.
Axolotl Tool Overview: Learn how Axolotl simplifies the fine-tuning process, supporting a range of models and configurations.
Hyperparameter Optimization: Gain insights into tweaking hyperparameters for optimal performance.
👨💻 Features of Axolotl:
Train models like llama, pythia, falcon, mpt.
Supports techniques including fullfinetune, lora, qlora, relora, and gptq.
Customize via YAML or CLI, handle various datasets, and integrate advanced features like xformer and multipacking.
Utilize single or multiple GPUs with FSDP or Deepspeed.
Log results to wandb, and more.
Whether you're a beginner or an experienced AI practitioner, this video equips you with practical knowledge and skills to fine-tune LLMs effectively. I'll guide you through each step, ensuring you grasp both the theory and application of these techniques.
👍 If you find this video helpful, please don't forget to LIKE and COMMENT! Your feedback is invaluable, and it helps me create more content tailored to your learning needs.
🔔 SUBSCRIBE for more tutorials on Gen AI, machine learning, and beyond. Stay tuned for more insights and tools to enhance your AI journey!
Join this channel to get access to perks:
#llm #generativeai #ai
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