AI Briefing#model-optimization
The latest AI and developer news about #model-optimization, with the original source and a short summary.
The latest AI and developer news about #model-optimization, with the original source and a short summary.
It covers data collection, fine-tuning, and optimization strategies for efficiently deploying VLA models on embedded robotic platforms.
Covers how to optimize and deploy LLMs on Intel hardware using Optimum-Intel and OpenVINO GenAI.
Inference performance of SD Turbo and SDXL Turbo models was significantly improved through ONNX Runtime and Microsoft Olive.
Boosted CPU serving performance 4x through torchserve thread configuration and ipex pinning.
Through Apple's Core ML optimizations and 6-bit palettization, Stable Diffusion can run faster and more efficiently on Apple devices.
This covers how to use SmoothQuant technology to reduce LLM size and improve inference efficiency on Intel CPUs.
Explains how to convert Transformers models to ONNX format and optimize inference performance using Hugging Face Optimum.
Hugging Face has released Optimum, an open-source library that supports hardware-specific performance optimization for transformer models.
Hugging Face has released the pytorch_block_sparse library, which reduces model size and increases speed.