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Use when writing HIP kernels with hipcc, porting CUDA code through HIPIFY, profiling with rocprofv3, debugging with rocgdb, or optimizing for MI300X. Not for NVIDIA-side kernels: use cuda.
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Use when writing, optimizing, or benchmarking a C++ CPU kernel with AVX2 or AVX512 intrinsics for the Hugging Face kernels ecosystem. Not for CUDA kernels: use cuda.
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窓際族物語の動画を、チャプター毎のキーフレーム画像を作らずに「プロンプト＋キャラクターシート＋セリフ音声」だけでMiniMax H3（R2V）に生成させるワークフロー。ユーザーから「画像なしで動画を作って」「キーフレームなしで動画を作って」「シートだけで動画を作って」と指示されたときに必ず使用する。大まかな指示→チャプター分割案→チャプター毎の動きを人間に確認→全チャプター確定→プロンプト作成の順で進め、確定前にプロンプトを書かない。画風の注意文はチャプターに映るものだけを最小限で書き、モブキャラはラン専用のキャラクターシートを作って渡す。動画生成の実行先は/colab-video（Colab）または/local-videoステップ7（ローカルCUDA）。
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帮助编写和检查PyTorch模型与训练代码，涵盖数据加载、实验可复现性、训练状态保存与恢复，并提供改善GPU显存占用和运行速度的方法。
使用TRL指导语言模型进一步训练，使回答更符合指令或人的偏好。涵盖数据准备、训练方式选择、奖励模型训练与评估，以及内存不足和训练不稳定时的调整。
帮助在NVIDIA GPU上加速大语言模型的响应生成，并通过API提供服务。支持量化、批量处理和多GPU分布运行，以调整显存占用与服务性能。
分析开放权重语言模型的拒答机制，支持无需重新训练的权重修改实验，并根据计算环境选择模型与方法，比较修改前后的拒答率和回答质量。
使用 NeMo Curator 整理大语言模型训练数据，去除重复和低质量内容，删除或替换个人信息。还涵盖处理流程的构建，以及图像、视频和音频数据的筛选整理。
用Python实现文字生成图像、现有图像风格转换和指定区域补全，并指导如何控制构图与风格、调整生成参数及减少内存占用。
指导使用 AudioCraft 选择模型并实现从文字生成音乐、音效和环境声，涵盖旋律及参考音频引导、立体声输出和批量生成。
根据歌词及曲风、乐器标签在本地生成歌曲，指导安装、模型下载、输入整理和节省显存的设置，并协助将生成结果保存为MP3文件。
Plan and review PufferLib reinforcement learning workflows, where agents learn through rewards. Covers version-specific environment checks, parallel execution, training and evaluation plans, plus checkpoint inspection before loading saved models.
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Generate candidate positions and orientations for small molecules binding to proteins with DiffDock and DiffDock-L. Prepare single or batch docking runs, organize predicted poses by model confidence, and review validation needs without treating confidence as binding affinity.
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Build and debug sglang tests comparing token probabilities during prompt processing and step-by-step generation. Separate numerical differences from reused-state bugs, choose test thresholds, and locate the first operation that diverges.
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Investigate SGLang serving failures by collecting server state, preserving failing requests, and replaying them on a clean target. Summarize evidence and likely causes, then choose the next debugging tool for crashes, stalls, or performance regressions.
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Organize SGLang GPU operations when adding or moving code. It guides API placement, operation registration, test organization, and migration checks to preserve existing behavior, references, and continuous integration coverage.
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Investigate phishing emails that hide malicious links in QR codes. Decode destinations, analyze suspicious URLs, configure email detection rules, and plan mobile protections and awareness training for people who scan codes on their phones.
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Convert LLMs to GGUF and reduce model size through quantization, which stores weights at lower numerical precision. Prepare models for CPU or Apple Silicon inference, compare compression levels, and integrate them with local AI tools.
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Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.
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Integrate Flash Attention into transformer models to reduce processing time and GPU memory use for long sequences. Covers PyTorch integration, advanced attention options, and benchmarks that compare speed, memory use, and output accuracy.
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Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.
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Use SAELens to train and analyze sparse autoencoders, which separate neural network activity into features that may represent recognizable concepts. Explore pretrained features, evaluate reconstruction quality, and investigate how individual features influence language model outputs.
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Set up LLM serving with structured responses, including JSON and outputs restricted to specified patterns. Covers reusing shared prompt calculations, tool calling, and multi-turn conversations through SGLang, with examples for server setup and application integration.
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Build image captioning, visual question answering, and text-based image search with BLIP-2. The skill explains model selection, batch processing, and memory-saving settings, with Python examples for connecting image understanding to language models.
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