'models' 검색 결과

Search Hugging Face Hub for models, datasets, papers, and AI apps. Inspect documentation, use hosted apps for image generation or transcription, and run or schedule code on cloud CPUs and GPUs through an MCP server connection.

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• Compare models for a specific task
• Find training datasets
• Generate images or transcribe audio with Spaces

Build JavaScript or TypeScript applications that run text, image, and audio models in browsers or server runtimes. Guides compatible model selection, GPU or CPU execution, caching, and memory cleanup without requiring a Python server.

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• Add text classification or translation to a browser app
• Implement image classification and object detection
• Transcribe audio using JavaScript

Read and summarize AI research papers from Hugging Face or arXiv links and identifiers. Retrieve paper content alongside structured details about authors, related models, datasets, Spaces, and implementation links to support further investigation.

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• Summarizing a paper from its URL or arXiv ID
• Finding models, datasets, and Spaces linked to a paper
• Checking authors and implementation links

Call Yao processes to query data models or run scripts. Check which operations are permitted, inspect their required arguments through the documentation workflow, and execute a process using its full name.

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• Check which Yao processes are permitted
• Query records through a data model
• Run a named Yao script

Apply a specific set of Rails conventions to design, implementation, and code review. It keeps business logic in models, represents state changes as records, limits dependencies, and uses Minitest with fixtures, or predefined test data.

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• Designing Rails models and database changes
• Representing state changes with separate records
• Reviewing code for consistent Rails conventions

Tests whether an AI agent accepts unverified plugins, tools, models, or updates. It checks risks introduced through external dependencies, including unsafe installation sources and missing version controls, and reports confirmed findings with conversation evidence.

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• Checking acceptance of plugins and tools from unknown sources
• Reviewing dependency version pinning and integrity checks
• Testing validation of user-supplied tool manifests

Use DeepChat's bundled command-line interface to compare models, generate media, transcribe audio, and extract text from images. Also inspect runtime status, artifact metadata, public settings, installed Skills, and MCP connections to external tools.

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• Comparing models and collecting benchmark results
• Generating images, video, or speech
• Transcribing audio and extracting text from images

Find and run over 2,000 AI models and API tools through one MCP connection, a shared interface for agents. Inspect inputs and pricing before generating media, searching the web, or retrieving structured and social data.

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• Finding and running image or video generation models
• Retrieving social media posts
• Searching the web and extracting page content

Compare how different attribution models, or ways of assigning conversion credit, change campaign results across Google and Meta. The skill highlights shifts in credited conversions and return on ad spend, then suggests budget adjustments based on the comparison.

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• Reviewing campaign budgets across attribution models
• Checking discovery campaigns before cutting their budgets
• Explaining campaign contributions in quarterly reviews

Choose statistical tests for research data, check their assumptions, and interpret effect sizes, which describe the magnitude of differences or relationships. This skill also supports sample size planning and produces results in APA reporting style, with figures and tables.

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• Choosing a test for group comparisons or relationships
• Checking normality, variance, and outliers before analysis
• Estimating the sample size needed for a study

Helps build a research framework by comparing theories, mapping relationships between variables, and deriving testable hypotheses. Explores less conventional options, examines weaknesses and competing explanations, and produces diagrams with anticipated reviewer objections.

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• Comparing theories for a research question
• Deriving hypotheses from relationships between variables
• Examining weaknesses and anticipated reviewer objections

Choose and interpret models for panel data, which tracks the same firms, people, or countries over time. Use Python, R, or Stata examples to compare fixed and random effects, check assumptions, and select standard errors.

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• Choosing between fixed and random effects
• Estimating entity and time effects together
• Selecting standard errors for correlated observations

Build a Python workflow for empirical research, from data cleaning and statistical checks to estimation, robustness checks, and paper-ready tables and figures. Economics is the default, with additional modes for epidemiology, public health, and machine learning for causal inference.

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• Building an end-to-end empirical analysis workflow in Python
• Replicating an applied economics paper
• Producing regression tables and event-study plots

Write and debug Python regressions with PyFixest using a reference for model formulas, estimation functions, and standard errors. It also covers extracting results, comparing models, and producing regression tables and plots, including models that account for group differences.

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• Write fixed-effects regression code
• Check clustered standard error settings
• Compare models in tables and plots

Build applications that exchange live audio, video, and text with the Gemini Live API. Covers voice conversations, streaming transcription and translation, session recovery, authentication, and migration from older Live models in Python or JavaScript/TypeScript.

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• Build an AI app for live voice conversations
• Add real-time transcription or speech translation
• Send camera or screen frames alongside audio

Design, debug, and tune OpenRouter API integrations. It checks model capabilities, reasoning settings, output limits, and fallback routes, then validates results against quality, cost, privacy, and performance requirements, including failed attempts.

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• Design an OpenRouter API integration
• Investigate empty, truncated, or malformed responses
• Configure fallback models and provider restrictions

Study and apply statistics, probability, and linear algebra through Python examples. Calculate summary statistics, compare groups, analyze A/B tests, and build regression models while checking assumptions and considering the size of observed differences.

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• Summarizing data with averages and measures of spread
• Comparing conversion rates in an A/B test
• Comparing regression models

Build and evaluate machine learning models in Python with scikit-learn. Covers classification, numerical prediction, grouping similar data, preprocessing, model comparison, and pipelines that combine data preparation and prediction into a repeatable workflow.

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• Build classification or numerical prediction models
• Group customers with similar characteristics
• Compare models using cross-validation

Build image and video AI systems for object detection and segmentation, which identifies separate image regions. The skill covers custom model training, dataset pipelines, inference optimization, real-time processing, and deployment and monitoring in production.

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• Developing object detection and image segmentation systems
• Training custom vision models with project data
• Optimizing inference for real-time video processing

Search current web information and recent scientific literature with Perplexity through OpenRouter and LiteLLM. Get answers with source citations, choose models for different research needs, and save results as JSON for further use.

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• Find recent scientific papers
• Verify claims with cited sources
• Compare current technologies and approaches

Supports pathology image analysis in Python, from loading whole-slide images to normalizing stain colors, identifying cell nuclei, mapping spatial relationships, and training machine learning models. It also covers multiplex imaging and marker measurements.

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• Preprocessing stained tissue images
• Detecting and segmenting cell nuclei
• Analyzing spatial relationships between cells

Choose embedding models, which turn text into numerical representations of meaning, for semantic search and AI answers grounded in retrieved documents. Includes examples for document splitting, preprocessing, multilingual content, and measuring retrieval quality.

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• Choosing an embedding model for document search
• Adjusting document chunk sizes and overlap
• Designing search across multiple languages

Add language model features to a PinMe TypeScript Worker through its OpenRouter proxy. The skill guides code for listing models, chat responses, streaming text as it arrives, web search, and handling API errors.

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• Add AI chat to a PinMe app
• Display generated responses as they arrive
• Let a model search the web

Deploy and manage NVIDIA RAG Blueprint, a system that retrieves information to support AI answers. Check the running environment, configure models and search features, troubleshoot unhealthy services, and verify affected workflows after changes.

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• Deploy NVIDIA RAG Blueprint
• Configure search and document ingestion
• Change models or enable image features