Tag: Hugging Face
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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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Hugging Face Air-Gapped: Enterprise Hub, Offline Mirroring, and the On-Prem Build (Hugging Face Series, Part 16)
How to run Hugging Face models on a segmented or air-gapped network: mirror the artifacts to local storage, force offline mode at runtime, and use the Enterprise Hub for identity, governance, and the rate limits a proxy needs.
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Hugging Face Security and Governance: Gated Models, Malicious Weights, and Scanning Before Anything Enters Your Registry (Hugging Face Series, Part 15)
A model download is an unvetted artifact, and a pickle checkpoint can run code the moment you load it. Here is how to gate the Hugging Face Hub the way you already gate a container registry: safetensors over pickle, no blind trust_remote_code, scan before promote, pin for provenance.
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Hugging Face Spaces and Gradio: Great for Demos, Wrong for Production (Hugging Face Series, Part 14)
A Hugging Face Space turns a model into a shareable Gradio demo in three files. Here is how a Space runs, a worked example, and the hard line where a demo must graduate to real serving.
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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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Text Generation Inference (TGI) in Production: A Real Serving Example (Hugging Face Series, Part 12)
TGI turns a Hugging Face model into an OpenAI-compatible endpoint with one docker run. Here are the flags that decide whether it fits your VRAM, how to consume it, and an honest verdict now that TGI is in maintenance mode and Hugging Face points new builds at vLLM.
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How to Run a Hugging Face Model: Inference Providers vs Endpoints vs Self-Host (Hugging Face Series, Part 11)
Three ways to serve a Hugging Face model: the serverless Inference Providers proxy, dedicated Inference Endpoints, or self-hosting TGI on your own GPUs. A build-vs-buy verdict for the infra team that owns the bill.
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Pushing Models and Datasets to the Hugging Face Hub: Private Repos, Versioning, and Model Cards (Hugging Face Series, Part 10)
Pushing to the Hugging Face Hub is artifact promotion. Here is how to create private repos, scope tokens and org RBAC, pin consumers to a commit, write a model card for provenance, and move large files without melting your bandwidth.
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Accelerate: Multi-GPU Training Without Rewriting Your Code (Hugging Face Series, Part 9)
How Hugging Face accelerate turns a single-GPU training script into a multi-GPU job, when to use DDP versus FSDP versus DeepSpeed, and what the choice means for interconnect, capacity, and cost on the boxes you run.
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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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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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