Transformer models are the powerful neural networks that have become the standard for delivering advanced performance behind these innovations. But there is a challenge: Training these deep learning models at scale and doing inference on them requires a large amount of computing power. This can make the process time-consuming, complex, and costly.

Today we will talk about all kinds of issues around accessible, production level AI solutions. We also talk about ethical questions around AI usage and why open, democratized AI solutions are important.

Learn more:
Hugging Face
https://huggingface.co

Hugging Face Hub
https://huggingface.co/models

Fast Inference on Large Language Models: BLOOMZ on Habana Gaudi2 Accelerator
https://huggingface.co/blog/habana-gaudi-2-bloom

Accelerating Stable Diffusion Inference on Intel CPUs
https://huggingface.co/blog/stable-diffusion-inference-intel

Transformer Performance with Intel & Hugging Face Webinar
https://www.intel.com/content/www/us/en/developer/videos/optimize-end-to-end-transformer-model-performance.html#gs.pomt5k

Intel Explainable AI Tools
https://github.com/IntelAI/intel-xai-tools

Intel Distribution of OpenVINO Toolkit
https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html

Intel AI Analytics Toolkit (AI Kit)
https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html

Guests:
Julien Simon – Chief Evangelist @ Hugging Face
Ke Ding – Principal Engineer @ Intel

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Tags: AI, ChatGPT, developers, Ethical AI, Gaudi, Habana, Hugging Face, Intel, Large language models, LLM, oneAPI, Open Source, OpenVINO, optimization, Stable diffusion
 
Posted in: Audio Podcast, Code Together, Intel