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Fix Python type checking failures found locally or in CI, the automated checks run on code changes. It identifies affected files, applies targeted fixes to type declarations and checks, then reruns validation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fix type checking failures reported by CI
• Resolve errors involving values that may be None
• Add a directory to automated type checking

Package an existing custom diffusers block into a directory ready for Hub upload. Select a class from a Python file and save its source, configuration, and associated files so others can load the block.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a custom block for Hub distribution
• Selecting one block class from a Python file
• Creating the directory layout needed to load a shared block

Review a diffusers contribution before opening a pull request. Check the full change against project rules, trace potentially unused code, and receive a severity-based report separating required fixes from points to discuss with reviewers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking a diffusers contribution before opening a pull request
• Investigating potentially unused code in a change
• Reviewing fixes again and preparing notes for reviewers

Integrate a model or pipeline into diffusers, from file structure and weight conversion to tests and documentation. Verify numerical agreement with the original implementation, with modular pipelines as the preferred starting point for new integrations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a new model and pipeline to diffusers
• Converting a pipeline to modular format
• Converting weights for a new version of a supported model

Run diffusers generation pipelines from a terminal and save their outputs. Inspect accepted inputs without downloading model weights, apply LoRA adapters during generation, run remotely through HF Jobs, and inspect software and GPU environment details.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running one-off generation or batch jobs from a terminal
• Checking a new model's basic operation
• Inspecting accepted inputs before running a pipeline

Check that changes to existing PEFT methods still work with saved training checkpoints. The skill guides compatibility tests, configuration checks, benchmark comparisons, and reporting, including comparisons of model outputs before and after a change.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking compatibility after a forward method rewrite
• Changing state_dict structure while supporting old checkpoints
• Reviewing defaults for new configuration fields

Add or review research paper entries in TRL’s paper index. Organize each paper’s contribution, its connection to the implementation, and a Python configuration example using training settings supported by the paper.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Documenting a newly implemented research method
• Checking paper index updates during PR review
• Recording training settings from a paper

Write and debug code for further training large language models with TRL. Match training methods to dataset formats, configure supervised or preference-based training, and set up reward functions through Python or the command line.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training a language model on example responses
• Training with preferred and rejected response pairs
• Debugging GRPO reward functions and batch settings

Train sentence-transformers models for search, similarity, and reranking, which reorders search results by relevance. Choose a model type, prepare data, configure training, compare results against a baseline, and publish the trained model to Hugging Face Hub.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fine-tuning an embedding model for domain-specific search
• Training a model to rerank search results
• Training SPLADE or ColBERT retrieval models

Build text, image, and audio features in JavaScript using machine learning models that run in browsers or server runtimes. Covers model selection, execution settings, caching, and memory cleanup without requiring a Python server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add sentiment analysis to a JavaScript app
• Build image classification in a browser
• Implement audio transcription

Build and maintain machine learning apps on Hugging Face Spaces. Choose a suitable app framework and computing hardware, load models, deploy files, and check logs and outputs to diagnose build or runtime problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Publishing a machine learning demo
• Adapting an existing Gradio app for ZeroGPU
• Diagnosing Space build or runtime failures

Build and publish a browser demo for a supplied LoRA, a model adaptation for image or video tasks. Tailor the Gradio interface to its inputs and follow the model’s recommended settings on Hugging Face Spaces.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Share a trained LoRA through a browser demo
• Build an interface tailored to image editing or video generation
• Apply recommended trigger words and generation settings

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Manage Hugging Face Hub through the hf command line tool. Download and upload models or datasets, organize cloud storage, manage authentication and local caches, run scheduled jobs, and deploy or maintain model inference services.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Download or publish models and datasets
• Sync training checkpoints to cloud storage
• Inspect and clean the local cache

Choose a serving container for models deployed on SageMaker. Match the model type to a compatible image, retrieve its address from the AWS catalog, and check regional availability, CPU or GPU variants, and required host settings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a container for an LLM deployment
• Select images for embeddings and rerankers
• Check regional image addresses and GPU compatibility

Fine-tune vision models on custom images using Hugging Face Jobs cloud GPUs. Covers object detection, image classification, and segmentation—identifying image regions—with dataset validation, hardware selection, cost estimates, monitoring, and saving trained models to the Hub.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train an object detector on custom images
• Fine-tune an image classifier
• Adapt SAM/SAM2 to custom image masks

Find AI models for a specific task using official Hugging Face benchmark leaderboards. Compare scores, model sizes, and licenses, with memory-based estimates of device suitability and flags for models requiring quantization or API access.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare coding or reasoning models by benchmark scores
• Find model candidates for a laptop's available memory
• Choose models for OCR or speech recognition

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Create interactive web demos and chat interfaces in Python with Gradio. Connect functions to text, image, audio, and file controls, arrange screen layouts, and define what happens when users click buttons or change inputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a Python function into an interactive demo
• Building a chatbot interface
• Adding image and audio inputs and outputs

Write and debug code for further training large language models with TRL. Match training methods to dataset formats, configure supervised or preference-based training, and set up reward functions through Python or the command line.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training a language model on example responses
• Training with preferred and rejected response pairs
• Debugging GRPO reward functions and batch settings

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

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

Estimate inference memory for models on Hugging Face Hub without downloading their weights. Check GPU capacity for Safetensors or GGUF files, with optional estimates for the KV cache, memory used to retain attention data during generation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check whether a model fits on a GPU
• Compare memory estimates for GGUF quantizations
• Estimate KV cache memory for a chosen context length

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