Inference Optimization

Table of Contents Introduction Why Inference Optimization Matters Fundamentals of Quantization 3.1 Floating‑Point vs Fixed‑Point Representations 3.2 Common Quantization Schemes 3.3 Quantization‑Aware Training vs Post‑Training Quantization Practical Quantization Workflows for LLMs 4.1 Using 🤗 Transformers + BitsAndBytes 4.2 GPTQ & AWQ: Fast Approximate Quantization 4.3 Exporting to ONNX & TensorRT Benchmarking Quantized Models 5.1 Latency, Throughput, and Memory Footprint 5.2 Accuracy Trade‑offs: Perplexity & Task‑Specific Metrics Introducing vLLM: High‑Performance LLM Serving 6.1 Core Architecture and Scheduler 6.2 GPU Memory Management & Paging Deploying Quantized Models with vLLM 7.1 Installation & Environment Setup 7.2 Running a Quantized Model (Example: LLaMA‑7B‑4bit) 7.3 Scaling Across Multiple GPUs & Nodes Advanced Strategies: Mixed‑Precision, KV‑Cache Compression, and Async I/O Real‑World Case Studies 9.1 Customer Support Chatbot at a FinTech Startup 9.2 Semantic Search over Billion‑Document Corpus Best Practices & Common Pitfalls 11 Conclusion 12 Resources Introduction Large Language Models (LLMs) have transitioned from research curiosities to production‑grade engines powering chat assistants, code generators, and semantic search systems. Yet, the sheer size of state‑of‑the‑art models—often exceeding dozens of billions of parameters—poses a practical challenge: inference cost. ...

As an expert NLP and LLM engineer, I’ll guide you from zero knowledge to hero-level proficiency with Transformers v2, Hugging Face’s revamped library for state-of-the-art machine learning models. Transformers v2 isn’t a completely new architecture but a major evolution of the original Transformers library, introducing optimized workflows, faster inference via integrations like FlashAttention-2 and vLLM, streamlined pipelines, an enhanced Trainer API, and seamless compatibility with Accelerate for distributed training.[3][1] This concise tutorial covers everything developers need: core differences, new features, hands-on code for training/fine-tuning/inference, pitfalls, tips, and deployment. By the end, you’ll deploy production-ready LLMs efficiently. ...