'hf' 검색 결과

Adapt large language models to specific tasks through fine-tuning, or additional training. Prepares and validates datasets, configures LoRA or QLoRA to train a small subset of parameters, and supplies training settings, evaluation scripts, and deployment optimization guidance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing and validating JSONL training data
• Choosing LoRA or QLoRA for available GPU memory
• Configuring training and monitoring overfitting

Check the AWS profile, region, account ID, and caller identity before starting AWS work. Read local configuration and validate credentials, then flag missing settings, expired access, or possible IAM restrictions associated with SSO authentication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check the AWS account and region before deployment
• Identify the active profile when none was specified
• Detect expired credentials or missing configuration

Prepare an isolated Python environment for SageMaker deployments and AWS automation. The skill selects a supported Python version, installs current AWS packages, and checks versions so installation and execution use the same environment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare a Python environment for SageMaker deployment
• Separate AWS automation dependencies from system Python
• Check installed AWS package versions

Plan how to serve a model on Amazon SageMaker AI. The skill uses model type, traffic, response time, and cost constraints to recommend a deployment route, check instance quotas, and coordinate specialized setup and deployment skills.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan AWS hosting for a language model
• Choose a serving route for an embedding model
• Plan asynchronous inference for image generation

Prepare SageMaker deployments and training by finding existing execution roles, checking trust settings, and reviewing required permissions. Guides role creation only when necessary and identifies permission issues that require an AWS administrator.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check execution roles before deploying a SageMaker endpoint
• Find an existing role when no ARN is provided
• Investigate IAM AccessDenied errors

Deploy SageMaker endpoints, the services that answer model requests, with automatic capacity scaling, CloudWatch alarms, and resource tags. Supports real-time and asynchronous inference, scaling down to zero instances, deployment checks, and resource cleanup.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploying a real-time model endpoint
• Scaling a sparse workload down to zero instances
• Running long inference jobs asynchronously

Find and run local AI models with llama.cpp. Choose GGUF model files and quantization levels that fit available memory, launch a command line session or local server, and test an OpenAI-compatible API on your computer.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find models to run on a local computer
• Choose quantization for available memory
• Launch and test a local OpenAI-compatible API

Register arXiv papers on Hugging Face Hub, connect them to models and datasets, and manage authorship claims and profile visibility. Create research articles from Markdown templates and convert them into HTML for sharing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Registering an arXiv paper on Hugging Face Hub
• Linking papers to models and datasets
• Managing authorship claims and profile visibility

Track machine learning experiments with Trackio by recording training measurements, viewing live dashboards, and flagging diagnostic events. Retrieve metrics and alerts through command-line queries, with JSON output for automated monitoring and experiment review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording training loss and accuracy
• Flagging loss spikes or stalled training
• Monitoring cloud training through a Space dashboard

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Explore Hugging Face datasets through the Dataset Viewer API. Preview and retrieve rows, search text, filter records, inspect sizes and column statistics, and find Parquet download links for further analysis or data extraction.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preview dataset contents and data splits
• Search text and filter dataset rows
• Inspect dataset sizes, statistics, and Parquet links

Train or fine-tune language and vision models on Hugging Face Jobs without a local GPU. Covers dataset checks, hardware and cost planning, training monitoring, Hub storage, and GGUF conversion for local use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fine-tune a language model without a local GPU
• Validate preference data before DPO training
• Estimate training costs and choose GPU hardware

Assess Bluetooth Classic devices such as older car kits, industrial sensors, and audio accessories. The skill guides device discovery, service inspection, authentication checks, and known vulnerability assessment, helping document exposed functions and device-specific findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing older in-car Bluetooth devices
• Checking authentication on industrial device services
• Reviewing exposure to BlueBorne and KNOB

Plan NVIDIA NuRec workflows that turn sensor recordings into renderable 3D scenes. This routing skill selects the relevant specialist skills for dataset downloads, conversion, training, rendering, and frame cleanup, and puts the steps in order.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan a reconstruction workflow from sensor recordings
• Find the steps to render an existing USDZ scene
• Choose a workflow for cleaning up rendered frames

Turn a selected screenshot, mockup, or generated image into a responsive website or web app. Recreate its layout and visual assets, implement core interactions, and compare the running result with the reference in a browser.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an interactive prototype from a mockup
• Recreate a screenshot as a responsive web page
• Implement a mobile screen from a selected reference

Route product design requests to focused skills for user research, experience reviews, visual exploration, prototype building, or sharing. This entry point selects the appropriate workflow rather than performing the design work itself.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding the right workflow for a product design request
• Routing a product flow review to an audit
• Starting visual exploration for a new prototype

Create websites, dashboards, games, and app interfaces from image-generated design concepts. The skill turns the accepted concept into reusable UI components, implements working controls, and checks browser rendering, interactions, and responsive layouts against the design.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a new website from visual concepts
• Create a dashboard or management interface
• Redesign the appearance of an existing site

Investigate unfamiliar codebases before fixing bugs, adding features, or refactoring. Follow relevant code paths, plan focused changes, and verify results, while recording useful discoveries in .notebook/ so later sessions can build on earlier investigation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate and fix bugs in unfamiliar code
• Add features following existing project conventions
• Trace and document authentication or other flows

Prepare Vesperfall game assets as paired transparent PNG cards and live 3D previews. Track each model’s actual source, expose available animations, and validate catalog displays, ground contact, and review pages through tests and browser checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding characters or equipment to the Vesperfall catalog
• Pairing transparent PNG cards with live 3D previews
• Reviewing animations and ground contact on an isolated page

Find the appropriate plugin for supported DeFi services, blockchain-based financial applications, using service names or native tokens. Guide installation with user confirmation, then pass the original request to the plugin, or show the available services.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find the plugin for a specific DeFi service
• Identify a service from its native token
• Browse supported DApps

Predict structures of protein, nucleic acid, and small-molecule complexes with ESMFold2, using single sequences or aligned related sequences. The skill also covers ESMC models for numerical sequence representations, mutation scoring, and predictions of contacts between residues.

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• Predicting complex structures from single sequences
• Assessing designed binders with ESMFold2-Fast
• Predicting structures using aligned related sequences

Generate a Stata .do file that compares every subset of optional control variables across parallel instances. The script ranks combinations by the focal variable’s absolute t-statistic, reports the top ten, and fits the highest-ranked model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing all subsets of optional control variables
• Keeping mandatory controls in every regression
• Preparing parallel regression runs in Stata

Assess how a pull request affects documentation, classify the required updates, and coordinate writing, technical verification, and editorial review. The result is an advisory comment with proposed changes for affected pages or document structure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding documentation gaps caused by code changes
• Drafting targeted corrections for existing documentation
• Planning new pages or table of contents changes

Draft huggingface_hub release notes from saved pull request JSON files. The skill groups changes, explains major updates, adds examples from documentation changes, and includes author credits and verified documentation links in a Markdown document.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting release notes from saved pull request data
• Explaining major features and breaking changes
• Adding command or API examples from documentation changes