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Found 31 Skills
Comprehensive guide for implementing Syncfusion WPF Tabbed Window control that combines SfChromelessWindow with SfTabControl for document-based applications. Use this skill when implementing chromeless tabbed windows, browser-style tabs, document management windows, or Visual Studio-style tabs in WPF. Covers tear-off tabs, floating tab windows, tab merging between windows, drag-drop tab reordering, and MVVM tab binding scenarios.
Implement web service integrations in B2C Commerce using LocalServiceRegistry. Use when calling external APIs, configuring service credentials in services.xml, handling HTTP requests/responses, or implementing circuit breakers. Covers HTTP, SOAP, FTP, and SFTP services.
Fine-tune any HuggingFace CV / VLM / LLM model on local NVIDIA GPUs inside an NGC PyTorch container. Use when the user wants to fine-tune a HuggingFace model (full or LoRA), train a vision / VLM / LLM model end-to-end, generate a reproducible HF training pipeline, smoke-test a HuggingFace model locally before scale-up, push a fine-tuned model to the HF Hub with a model card, or emit a self-contained rerun skill for an existing HuggingFace finetune. Supports image classification, object detection, semantic / instance / panoptic segmentation, depth estimation, image-text-to-text VLM (SFT / LoRA), and LLM SFT / DPO / GRPO. Six-step workflow: inspect and qualify, hardware and NGC image, research, generate and smoke, train + eval + infer, push and emit rerun skill.
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
Implement Syncfusion Blazor notification components (Toast, Message, Skeleton) for user feedback and loading states. ALWAYS use this when users need toast notifications, popup messages, alert boxes, success/error/warning/info messages, loading skeletons, shimmer effects, content placeholders, or any feedback UI. Trigger immediately when users mention notifications, toasts, alerts, messages, loading states, skeleton screens, shimmer loading, user feedback, status messages, SfToast, SfMessage, SfSkeleton, notification popups, or need to show temporary messages, form validation feedback, or loading placeholders.
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for...
Implements the Syncfusion WPF SfTextInputLayout control to provide floating labels, assistive labels, and input validation UI for WPF text inputs. Use when adding floating labels, customizing input container styles, or showing validation/helper text.
Plan Nemotron customization pipelines from repo steps: SFT, PEFT/LoRA, AutoModel vs Megatron-Bridge, DPO/RLVR/GRPO/RLHF, curate-then-translate, BYOB/MCQ benchmark prep or translation, checkpoint conversion, ModelOpt optimization, and endpoint or checkpoint evaluation.
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
Build interactive timeline components that display events and milestones sequentially. Use SfTimeline with alignment controls, custom items, and event handlers. This skill covers timeline configuration and customization for creating visual timelines in Blazor applications.
Validate and use packed sequences and long-context training in Megatron-Bridge, distinguishing offline packed SFT for LLMs from in-batch packing for VLMs, and applying the right CP constraints.