203 个 · 按相关度排序
Patterns for running AI models locally in browsers using WebGPU, Transformers.js, WebLLM, and ONNX Runtime. Zero API costs, full privacy. Use when "on-device AI, browser AI, WebLLM, Transformers.js, WebGPU, edge inference, offline AI, client-side ML, ONNX web, " mentioned.
中文简介正在准备中。当前显示原文说明。
Design practical electromagnetic devices including electromagnets, DC and brushless motors, generators, and transformers by bridging theory to application. Use when sizing a solenoid or toroidal electromagnet for a target field or force, selecting motor topology and computing torque and efficiency, designing a transformer for a given voltage ratio and power rating, or analyzing losses from copper resistance, core hysteresis, and eddy currents.
中文简介正在准备中。当前显示原文说明。
为 msModelSlim 创建基础 Transformers 模型适配器（Model Adapter）。 包含创建适配器、实现必需接口与注册安装流程。 适用：Decoder-only LLM、理解类 VLM（仅 LLM/text 部分）。 多模态生成（DiT/扩散，含双/多专家）见下方「多模态生成扩展」节，按需加载。 不适用：Encoder-only、非 Transformers 架构。
中文简介正在准备中。当前显示原文说明。
Garanta estrutura válida de JSON/XML/código durante a geração, use modelos Pydantic para outputs type-safe, suporte modelos locais (Transformers, vLLM) e maximize velocidade de inferência com Outlines - biblioteca de geração estruturada da dottxt.ai
中文简介正在准备中。当前显示原文说明。
Framework para embeddings de última geração em sentenças, textos e imagens. Oferece 5000+ modelos pré-treinados para similaridade semântica, clustering e retrieval. Suporta modelos multilíngues, específicos de domínio e multimodais. Use para gerar embeddings para RAG, busca semântica ou tarefas de similaridade. Melhor para geração de embeddings em produção.
中文简介正在准备中。当前显示原文说明。
Modelo de espaço de estados com complexidade O(n) versus O(n²) dos Transformers. Inferência 5× mais rápida, sequências de milhão de tokens, sem cache KV. SSM seletivo com design aware de hardware. Mamba-1 (d_state=16) e Mamba-2 (d_state=128, multi-head). Modelos 130M-2.8B no HuggingFace.
中文简介正在准备中。当前显示原文说明。
Deploy and use the Exasol Transformers Extension AI Functions for NLP inference inside Exasol with notebook-connector. Covers initialize_te_extension, model installation, activation SQL, validation, and safe use of large models.
中文简介正在准备中。当前显示原文说明。
Run Hugging Face models in JavaScript or TypeScript with Transformers.js in Node.js or the browser.
中文简介正在准备中。当前显示原文说明。
Expert guidance for deep learning, transformers, diffusion models, and LLM development with PyTorch, Transformers, Diffusers, and Gradio.
中文简介正在准备中。当前显示原文说明。
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
中文简介正在准备中。当前显示原文说明。
Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.
中文简介正在准备中。当前显示原文说明。
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
中文简介正在准备中。当前显示原文说明。
Work with state-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks using HuggingFace Transformers. This skill should be used when fine-tuning pre-trained models, performing inference with pipelines, generating text, training sequence models, or working with BERT, GPT, T5, ViT, and other transformer architectures. Covers model loading, tokenization, training with Trainer API, text generation strategies, and task-specific patterns for classification, NER, QA, summarization, translation, and image tasks. (plugin:scientific-packages@claude-scientific-skills)
中文简介正在准备中。当前显示原文说明。
Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.
中文简介正在准备中。当前显示原文说明。
指导使用Axolotl对大语言模型进行微调，涵盖YAML配置、数据格式和训练方法，也可用于检查多GPU设置、排查训练故障及运行缓慢的问题。
使用JSON、正则表达式、Pydantic类型定义或固定选项约束LLM输出，将文字中提取的信息和分类结果整理为便于后续程序使用的格式。
帮助您使用Unsloth，以LoRA和QLoRA微调语言模型，探索减少GPU内存占用的训练方式，并依据官方文档整理的参考资料查询功能与API、实现代码和排查错误。
协助配置vLLM，将大语言模型作为API提供服务或批量生成文本，涵盖部署、性能监控、量化和多GPU分工，也用于排查显存不足与响应缓慢。
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。
Track machine learning experiments in W&B, record training settings and metrics, and compare results in dashboards. Automate searches for training settings and keep version histories for models and datasets.
中文简介正在准备中。当前显示英语简介。
使用统一任务评估大语言模型的知识、推理和代码生成能力，生成多模型对比表及训练阶段的成绩趋势，并保存评估结果和每道题的回答。
以少量修改让现有PyTorch训练代码适用于多GPU或多台机器，指导混合精度、节省内存的DeepSpeed与FSDP配置，以及训练启动方法。
使用Chroma构建按语义相似度检索文档的系统，保存和更新正文及附加信息，按条件筛选结果，并用于开发结合检索资料的AI应用原型。
帮助安装和使用LLaVA，为照片生成说明、回答图像内容问题，并支持围绕同一图像持续对话、理解带图文档，以及选择模型和配置量化以降低内存占用。