'models' 검색 결과

Build molecular property prediction workflows with DeepChem for solubility, toxicity, and related chemistry tasks. Prepare molecular representations, choose compatible models, handle missing labels, and evaluate predictions using data splits designed for the intended scientific question.

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• Predicting solubility from molecular structures
• Comparing graph models on MoleculeNet datasets
• Checking data splits for information leakage

Generate photos, illustrations, logos, and presentation visuals through the OpenRouter Image API. Edit or combine reference images, choose among supported models, and check available settings such as resolution, output format, and transparency before generation.

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• Creating illustrations and visuals for slides or posters
• Editing colors or styles in existing images
• Combining multiple reference images

Helps implement document processing in TypeScript with Azure, extracting text, tables, invoice fields, and receipt details. Covers ready-made models, custom model training, document classification, authentication, and waiting for analysis results.

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• Extracting text and tables from PDFs
• Reading invoice and receipt fields
• Training models for specialized documents

Build .NET workflows that extract text, tables, and fields from invoices, receipts, and other documents using Azure AI Document Intelligence. Covers prebuilt models, custom model creation, document classification, authentication, and result handling.

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• Extract vendors, line items, and totals from invoices
• Read merchants, dates, and totals from receipts
• Extract text and table cells from documents

Choose embedding models, which represent text as numerical vectors, for meaning-based search and RAG applications. Compare options for code, prose, and multilingual content, and plan document splitting, preprocessing, caching, and dimension reduction to improve retrieval quality.

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• Choosing an embedding model for RAG
• Reviewing document chunk size and boundaries
• Planning code or specialist document search

Design query-ready data from streams of recorded events. The skill guides read models, databases shaped for queries, with practices for replaying events, resuming after failures, and tracking delays in dashboards or search indexes.

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• Build read models for systems that separate reads and writes
• Create dashboard data from recorded events
• Build search indexes from event streams

Design dbt data transformations with clearly organized models, data quality tests, documentation, and incremental processing that updates only part of a dataset. Use it to structure analytics tables and organize a dbt project.

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• Organizing a dbt project into model layers
• Adding data quality tests
• Designing incremental updates for large tables

Connect ViewModels, objects that manage screen state and actions, through messages without direct references. The skill covers event broadcasts, requests for values, window-specific channels, and recipient registration and cleanup using CommunityToolkit.Mvvm.

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• Share login or theme changes across ViewModels
• Request values from another ViewModel
• Keep messages within a specific window

Connect CommunityToolkit.Mvvm ViewModels to Microsoft.Extensions.DependencyInjection, which supplies their required services. Get guidance on startup setup, instance lifetimes, messaging, view integration, and replacing dependencies in tests across supported .NET user interface frameworks.

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• Set up dependency injection in a new XAML app
• Choose shared or per-page service instances
• Diagnose missing service registration errors

Create and analyze AI model evaluation experiments in Arize using the ax CLI, a command-line tool. Collect real model outputs, export results, compare evaluation scores, and identify examples where a new model performs worse.

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• Comparing models on the same evaluation dataset
• Finding examples that perform worse after a model change
• Exporting experiment results for further analysis

Build Avalonia screen logic, navigation, and step-by-step wizards using Zafiro and ReactiveUI. The skill guides ViewModels, which manage screen state and behavior, plus commands, automatic section discovery, and connections between views and their logic.

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• Organizing screen state and commands in Avalonia
• Building a wizard with multiple steps
• Setting up section-based navigation

Create interactive 3D scenes for websites, including product views and scroll-driven animations. Covers choosing Three.js, React Three Fiber, or Spline, preparing lightweight models, showing loading progress, and adapting experiences for mobile devices.

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• Building an interactive 3D product configurator
• Adding 3D scenes to a portfolio
• Animating models and cameras as visitors scroll

Plan and configure large language model training with OpenRLHF to better match human preferences. Covers training method selection, distributed GPU setup, inference acceleration with vLLM, and troubleshooting memory or training stability issues.

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• Choosing a method for training models on human preferences
• Setting up PPO training with a reward model
• Configuring GRPO training without a critic model

Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.

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• Generate document embeddings for RAG
• Find sentences with similar meanings
• Group documents by topic

Use MLflow to record machine learning experiments, compare results, and manage model versions. The skill covers logging training settings, metrics, and files, organizing a model registry, and preparing models for local or cloud deployment.

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• Comparing training settings and model performance
• Recording experiments for later reproduction
• Managing model versions and release stages

Evaluate language models with over 100 benchmark tests covering knowledge, math, coding, safety, and image understanding. Configure runs on Docker, Slurm computing clusters, or cloud infrastructure, compare models, and export results.

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• Compare models using the same benchmark tests
• Run large evaluations on a Slurm cluster
• Evaluate safety and image understanding

Deploy language models with vLLM through an OpenAI-compatible API or offline batch processing. Tune response speed, throughput, and GPU memory use with quantization, which reduces numerical precision, and execution across multiple GPUs.

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• Deploy an OpenAI-compatible language model API
• Generate text from many prompts in a batch
• Serve quantized models with limited GPU memory

Configure LLM inference and serving on NVIDIA GPUs with TensorRT-LLM. Covers quantization, which reduces numerical precision, batching requests during generation, and distributing models across GPUs to improve processing capacity and response times.

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• Setting up an LLM server on NVIDIA GPUs
• Configuring FP8 or INT4 quantization
• Distributing large models across multiple GPUs

Helps speed up LLM text generation by comparing methods that predict and verify multiple tokens at once. Covers draft model selection, Medusa prediction heads, Lookahead Decoding settings, and deployment examples for inference services.

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• Reducing chatbot response latency
• Comparing methods for faster code generation
• Selecting a small draft model for a larger LLM

Implement or modify Composio's Python SDK, the code used to integrate its features into Python applications. Covers tools, sessions, authentication settings, connected accounts, client integration, and shared models under python/composio.

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• Implement or modify Python tools and toolkits
• Update session and authentication behavior
• Revise client integration and shared Python models

Analyzes marketing campaign data to compare channel contributions, find drop-offs before purchase, and calculate returns and acquisition costs. Uses five attribution models, which assign credit to customer interactions, with results available as readable summaries or JSON.

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• Compare channel contributions across attribution models
• Find where prospects drop out before purchase
• Compare campaign returns and acquisition costs

Prepare trained machine learning models for production with deployment, monitoring, and retraining workflows. Build RAG systems that retrieve documents for answers, and integrate language model APIs with retries, fallback providers, and cost tracking.

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• Deploying a trained model to production
• Setting up model monitoring and retraining workflows
• Building a RAG pipeline for document-based answers

Design product pricing based on customer value, competitor offerings, and willingness to pay. Compare charging models, define plan tiers and included features, and outline experiments to test prices and assumptions before introducing a new pricing structure.

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• Setting prices for a new product
• Comparing flat-rate and usage-based pricing
• Designing free and paid plan tiers

Evaluate a technology adoption question, a document, or proposed approaches against project evidence and constraints. Receive a supported position with tradeoffs and uncertainty, with an optional panel that consults other models and reconciles their views.

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• Assessing whether to adopt an external technology
• Getting an overall assessment of a document
• Comparing a supplied set of approaches