Tag: transformers
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Which Model for What: a Hugging Face Model Map for Text, Vision, Audio and Video (Hugging Face Series, Part 17)
A task-to-model map for Hugging Face: which model family to use for chat, search, transcription, speech, captioning, image and video, with sizes, licenses, the right library, and the GPU it needs.
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optimum and Quantization: ONNX, GPTQ and AWQ for the GPUs You Have (Hugging Face Series, Part 13)
Quantization is how you fit a model on the GPUs you already own. A field guide to optimum, ONNX Runtime, and 4-bit GPTQ vs AWQ, written for the infra engineer who has to make the capacity math work.
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Fine-Tuning with Trainer and LoRA/PEFT: When You Cannot Afford a Full Fine-Tune (Hugging Face Series, Part 8)
Full fine-tuning a 7B model can need two 80GB GPUs you do not have. Here is how the Trainer, LoRA, and QLoRA change the capacity math, with runnable code and the failure modes.
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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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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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Attention, the Idea That Made Modern AI Work (GenAI Series, Part 8)
How attention lets every word in a sentence weigh every other word, why it replaced slow left-to-right models, and why running in parallel is what let AI scale.
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How We Got from If-Statements to ChatGPT (GenAI Series, Part 3)
AI did not appear overnight. The road to ChatGPT runs through four eras, hand-written rules, machine learning, deep learning, and the 2017 transformer, explained in plain English.
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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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