Results for “h5”

Read and write large cuPyNumeric arrays to HDF5 files using Legate's parallel, distributed HDF5 I/O.

Single-cell RNA-seq analysis. Load .h5ad/10X data, QC, normalization, PCA/UMAP/t-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scRNA-seq analysis.

Single-cell RNA-seq analysis. Load .h5ad/10X data, QC, normalization, PCA/UMAP/t-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scRNA-seq analysis.

Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, clustering, marker gene identification, visualization, and trajectory analysis.

统一处理网页应用的生成、编辑，以及围绕已生成产物的问答。既负责把自然语言需求端到端转成可运行、可预览、可交付的网页应用产物，也负责在用户追问产物时基于真实产物作答。当用户要生成网站、H5、网页应用、管理后台、数据看板时使用。当用户要编辑已有网页应用、做功能新增、页面调整或 Bug 修复时使用。当用户提供 PRD、文档、截图或素材包并要求产出可预览网页应用时使用。当用户针对已生成的网页应用，要求总结或解读网页内容、查看或分析源码、解释或排查运行报错、查询访问量/用户量/数据报表/发布状态等运营信息时同样使用。

Análise de RNA-seq de célula única. Carregue dados .h5ad/10X, QC, normalização, PCA/UMAP/t-SNE, clustering Leiden, genes marcadores, anotação de tipo celular, trajetória, para análise de scRNA-seq.

Create, read, manipulate, and store annotated data matrices using the AnnData Python package, designed for single-cell genomics and general-purpose annotated data workflows.

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

Build and operate a Havoc C2 framework for authorized red-team engagements, including team server deployment, evasive Demon agent generation, and post-exploitation.

This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.

Estimates the memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub, using HTTP Range requests without downloading weights.

Runs batch processing and high-performance computing (HPC) workloads on Google Kubernetes Engine (GKE), including job queues, parallel processing, and MPI workloads.

Selects the correct SageMaker serving container image URI for HuggingFace model deployments, prioritizing HuggingFace-curated Deep Learning Containers over generic alternatives.

Comprehensive guide to t5. Master the concepts, implementation, best practices, and real-world applications of t5 in professional environments.

Quantize large language models to 8/4/3/2/1-bit precision without calibration data, using multiple optimized backends for deployment with vLLM or HuggingFace Transformers.

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub

Configure Hardware Security Modules (HSMs) using the PKCS#11 standard interface for key generation, signing, encryption, and key management with both physical HSMs and SoftHSM2 for development.

Deploy and configure the Havoc C2 framework with teamserver, HTTPS listeners, redirectors, and Demon agents for authorized red team operations.

Estimates GPU memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub using HTTP Range requests, without downloading weights locally.

This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

Quantize large language models to 8/4/3/2/1-bit precision without calibration data, using multiple optimized backends and integrations with HuggingFace Transformers, vLLM, and PEFT/LoRA.

Implements a Python function that converts an RGB image to HSV color space and extracts a specified channel (H, S, or V), including handling to convert the image to uint8 type to avoid OpenCV depth errors.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.

Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.

P5 namespace for Netsnek e.U. creative coding platform. Interactive sketch creation, generative art, and visual programming.

Build, deploy, and interact with Juicebox V5 projects, including revnets, hooks, and omnichain deployments, with API references, implementation details, UI generation, and GraphQL queries.

Detect process injection techniques (T1055) including CreateRemoteThread, process hollowing, and DLL injection via Sysmon Event IDs 8 and 10 and EDR process telemetry.