This course covers advanced techniques for reducing the size and increasing the inference speed of large language models (LLMs). Gain expertise in state-of-the-art methods like quantization, pruning, knowledge distillation, parameter-efficient fine-tuning (PEFT), and hardware-specific optimizations. Implement and evaluate these techniques to deploy LLMs effectively in resource-constrained environments. Intended for experienced AI engineers and researchers seeking to optimize LLM performance and efficiency.
Prerequisites LLM and Deep Learning experience
Level:
Optimization Analysis
Analyze the complex trade-offs between various LLM compression and acceleration methodologies.
Advanced Quantization
Implement and evaluate sophisticated quantization techniques, including sub-4-bit precision and QAT.
Sophisticated Pruning
Apply and compare advanced structured and unstructured pruning strategies for LLMs.
Knowledge Distillation
Design, implement, and evaluate knowledge distillation pipelines tailored for large language models.
PEFT Methods
Utilize and adapt various Parameter-Efficient Fine-Tuning methods like LoRA and QLoRA.
Hardware Optimization
Optimize LLM inference performance targeting specific hardware architectures.
Performance Evaluation
Rigorously evaluate the performance, fidelity, and efficiency impacts of optimization techniques.
Integrated Deployment
Integrate multiple optimization techniques into practical LLM deployment workflows.
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