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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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safetensors and Model File Formats: Why the Format Is a Security Decision (Hugging Face Series, Part 7)
A PyTorch .bin checkpoint is a pickle, and loading one can run code on your host. Here is why safetensors fixes that by design, how to convert and load safely, and where to scan models before they reach your GPU hosts.
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The Hugging Face datasets Library: Loading, Streaming, and Disk Survival (Hugging Face Series, Part 6)
The datasets library is a data-movement and storage problem before it is a data-science one. Here is how loading, streaming, and the Arrow cache actually behave, and how to keep them from filling your disk.
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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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Harbor for Beginners, Part 12: Putting It All Together in a Private AI Workflow
Harbor for Beginners · Part 12 of 12 Putting It All Together in a Private AI Workflow This is the finale. Across eleven parts you learned Harbor one piece at a time: projects, pushes, scans, gates, members, robots, retention, storage, and the way it talks to other registries. Now we step back and watch all…
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Harbor for Beginners, Part 11: Replication and Proxy Cache
Harbor for Beginners · Part 11 of 12 Replication and Proxy Cache By now you have pushed images, scanned them, locked them down, and cleaned them up, all inside your own project. This part looks outward. Harbor does not have to live on its own island. It can talk to other registries in two different…
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Harbor for Beginners, Part 10: See Storage and Free Up Space
Read how much space your project uses, see where it comes from, understand what a quota does, and free space by deleting an artifact, with a real before-and-after drop from 708 MiB to 306 MiB.
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Harbor for Beginners, Part 9: Set Retention and Immutability Rules
Keep your project tidy and safe. Set a retention rule to auto-clean old versions (preview it with a dry run), put it on a schedule, then lock important tags with an immutability rule so they can never change.
Architect’s Toolkit
PJ’s Tools
VMware Cloud Foundation
- VCF Documentation
- VCF 9 Planning & Preparation Workbook
- VCF Bill of Materials (BoM)
- VMware Compatibility Guide
- VMware Interoperability Matrix
- VMware Configuration Maximums
- VMware Ports & Protocols
- VMware Hands-on Labs
- RVTools Download
Nutanix
AI & Cloud-Native Platform
- NVIDIA Build (Model Catalog)
- NVIDIA AI Enterprise Reference Architecture
- NVIDIA NIM Performance Benchmarking
- NVIDIA NGC Catalog
- NeMo Microservices Helm Chart
- Helm Charts Repository
- Hugging Face Models
Architecture & Design
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.
Recommendations & Endorsements
Pranay has very deep technical knowledge and exceptional capabilities. He has strong ability to well-manage complex situations and deliver the customer expectations with high standards of satisfaction. We worked together in one of most complex migration projects and Pranay delivered flawlessly.
Pranay was my VMware Architect on one of our financial accounts at IBM. He was instrumental in documenting and recommending our reference architecture. As a Chief Architect, you are only as good as your team that supports you. Pranay was top notch.
Pranay is proactive, result oriented, responsible and technically sound. Always ready to put all his energy to get the job done. Exceptional troubleshooting skills in Windows Server, virtualization and VMware — a great asset to any company.
Pranay is one of the most talented and dedicated persons I have ever worked with. His vast knowledge from different domains enables him to discuss and help on almost every topic in IT. Needless to say, he is expert of experts in VMware.
DrJha