What techniques are used for model compression?

This skill covers quantization (reducing numerical precision), pruning (removing redundant weights), and knowledge distillation (training smaller models from larger ones) to reduce model size.

How can I reduce inference latency for Large Language Models?

It provides implementation patterns for KV caching, semantic caching, operator fusion, and dynamic batching to maximize hardware utilization and speed.

Does this skill help with edge deployment?

Yes, it includes specific patterns for deploying models on resource-constrained hardware like NPUs, mobile CPUs, and custom accelerators while maintaining efficiency.

Which optimization frameworks are supported by this skill?

The skill offers expert guidance for major frameworks including NVIDIA TensorRT, Microsoft ONNX Runtime, Intel OpenVINO, Apple Core ML, and TFLite.

ML Inference Optimization

Name: ML Inference Optimization
Author: melodic-software

bymelodic-software

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Ciencia de Datos y ML

Optimizes machine learning model performance by reducing latency and memory footprint through advanced compression and deployment strategies.

Acerca de

The ML Inference Optimization skill provides specialized expertise for streamlining machine learning models for production and edge environments. It guides users through complex techniques such as knowledge distillation, weight pruning, and quantization to balance accuracy against latency trade-offs. Beyond model architecture, the skill assists in implementing compiler-level optimizations with frameworks like TensorRT and ONNX, designing efficient runtime batching strategies, and deploying semantic caching layers to minimize redundant computation across various hardware accelerators.

Características Principales

Configuration of graph-level optimizations and operator fusion for inference engines
Design of sophisticated inference caching and dynamic batching strategies
12 GitHub stars
Guidance on knowledge distillation to create efficient student models
Implementation of model compression techniques including quantization and pruning
Cross-platform deployment patterns for edge devices and cloud hardware accelerators

Casos de Uso

Optimizing throughput for high-volume model-serving environments using TensorRT or ONNX
Shrinking model sizes for deployment on resource-constrained mobile and edge devices
Reducing Large Language Model (LLM) latency for real-time application responses

Acerca de

Características Principales

Configuration of graph-level optimizations and operator fusion for inference engines
Design of sophisticated inference caching and dynamic batching strategies
12 GitHub stars
Guidance on knowledge distillation to create efficient student models
Implementation of model compression techniques including quantization and pruning
Cross-platform deployment patterns for edge devices and cloud hardware accelerators

Casos de Uso

Optimizing throughput for high-volume model-serving environments using TensorRT or ONNX
Shrinking model sizes for deployment on resource-constrained mobile and edge devices
Reducing Large Language Model (LLM) latency for real-time application responses