Tag: Hugging Face
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Hugging Face Tokenizers: Context Limits, Token Budgets, and Capacity (Hugging Face Series, Part 5)
Tokenizers turn text into the integers a model reads, and they decide your context limit, throughput and token bill. An infra-first guide to encoding, batching, padding waste, and what changed in Transformers v5.
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Running Your First Model with Transformers Pipelines (Hugging Face Series, Part 4)
The transformers pipeline is the shortest path from a model repo to a running inference call. Here is what actually loads onto your box, and how device, device_map and dtype decide whether a model fits your GPU.
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Hugging Face Access Tokens and the hf CLI: Credentials Done Right (Hugging Face Series, Part 3)
A Hugging Face access token is a credential, not a convenience. Here is how to scope, store, rotate and use tokens with the new hf CLI, written for the infrastructure engineer who already runs registries and secret stores.
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Navigating the Hugging Face Hub: Models, Datasets, Spaces, and How to Read a Model Card (Hugging Face Series, Part 2)
The model card is provenance and license metadata you must vet before any model enters your environment. Here is how an infrastructure engineer reads the Hub fast and decides what to reject.
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What Hugging Face Actually Is: the Hub, the Libraries, and the Map (Hugging Face Series, Part 1)
Hugging Face is a registry, a set of open-source libraries, and a company. For infrastructure engineers moving into AI, here is the whole platform mapped onto systems you already run.
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About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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