Hugging Face

对本地或持续集成中发现的 Python 类型检查错误进行分类，通过类型收窄和注解修正等方式解决并重新验证，也处理新增类型检查范围的工作。

• 修复持续集成中的类型检查失败
• 修复包含 None 的类型错误
• 增加类型检查目录

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.

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• 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.

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

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.

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

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.

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

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.

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

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.

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

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.

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

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.

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• 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.

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

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.

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

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.

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

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.

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

Build reusable command-line scripts that fetch and process Hugging Face API data. Combine multiple requests, enrich model information, and produce outputs for further filtering, with built-in help and usage examples for repeated tasks.

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• Automating repeated model and dataset lookups
• Combining trending models with metadata and paper links
• Extracting licenses and tags from model cards

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.

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

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.

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

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

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.

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

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.

中文简介正在准备中。当前显示英语简介。

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

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

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

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.

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

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.

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• 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

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.

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