Deploying Quantized LLMs for Efficient Inference
Chapter 1: Advanced LLM Quantization Fundamentals
Revisiting Quantization Principles for Large Models
Low-Bit Quantization Techniques (Below INT8)
Understanding Quantization Data Types and Formats
Post-Training Quantization (PTQ) Algorithms for LLMs
Quantization-Aware Training (QAT) Considerations
Mixed-Precision Quantization Strategies
Calibration Data Selection and Preparation
Hands-on Practical: Applying GPTQ to an LLM
Chapter 2: Implementing LLM Quantization with Toolkits
Overview of LLM Quantization Libraries
Using bitsandbytes for Low-Bit Operations
Quantization with Hugging Face Transformers and Accelerate
Applying GPTQ using AutoGPTQ
Applying AWQ using AutoAWQ
Comparing Toolkit Outputs and Performance
Handling Model Compatibility Issues
Practice: Quantizing Models with Multiple Toolkits
Chapter 3: Performance Evaluation of Quantized LLMs
Metrics for Quantized Model Evaluation
Measuring Inference Latency and Throughput
Assessing Memory Consumption (Disk and Runtime)
Evaluating Accuracy Degradation
Benchmarking Frameworks and Tools
Analyzing Performance on Target Hardware
Visualizing Performance Trade-offs
Hands-on Practical: Benchmarking a Quantized LLM
Chapter 4: Optimizing and Deploying Quantized LLMs
Inference Optimization Techniques Post-Quantization
Choosing the Right Deployment Framework
Deploying with Text Generation Inference (TGI)
Leveraging vLLM for High-Throughput Inference
GPU Optimization with NVIDIA TensorRT-LLM
Deployment using ONNX Runtime
Containerization and Scaling Strategies
Monitoring Deployed Quantized Models
Hands-on Practical: Deploying via an Inference Server
Chapter 5: Addressing Advanced Challenges
Mitigating Accuracy Loss in Low-Bit Regimes
Handling Activation and Weight Outliers
Quantizing Specific LLM Components (Attention, Normalization)
Hardware Constraints and Kernel Availability
Dynamic Quantization vs. Static Quantization Trade-offs
Debugging Quantization Issues
Integrating Quantized Models into Production Pipelines
Practice: Fine-tuning Quantization Parameters
Applying AWQ using AutoAWQ
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- AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration, Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, Song Han, 2023 MLSys 2024 DOI: 10.48550/arXiv.2306.00978 - Introduces the foundational Activation-Aware Weight Quantization (AWQ) algorithm for compressing large language models by preserving salient weights.
- AutoAWQ GitHub Repository, Casper Hansen and contributors, 2024 (GitHub) - Official repository for the AutoAWQ library, providing installation instructions, API documentation, and examples for applying AWQ to Hugging Face models.
- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers, Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh, 2022 ICLR 2023 DOI: 10.48550/arXiv.2210.17323 - Presents GPTQ, another prominent post-training quantization method for LLMs, directly compared with AWQ in the course content.