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Learn how GPT language models work by preparing text, training a small model, and generating samples. Covers CPU experiments, custom datasets, further training of pretrained GPT-2 models, and reproducing GPT-2 with multiple GPUs.
中文简介正在准备中。当前显示英语简介。
Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.
中文简介正在准备中。当前显示英语简介。
Set up Langfuse to record LLM application activity, costs, and response times. Manage prompt versions, organize evaluation datasets, and compare results to investigate failures and guide improvements using recorded evidence.
中文简介正在准备中。当前显示英语简介。
Use W&B to record machine learning experiments, visualize training, and compare results. The skill also covers automated searches for training settings and version tracking for datasets and models, helping teams share experiments and trace their inputs.
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Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.
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Operate cognee from the terminal to store, retrieve, update, and remove information. Covers dataset and configuration management, ingestion cost estimates, session history access, and commands for inspecting or applying database migrations.
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Query documents and conversations stored in cognee to retrieve answers, supporting passages, or context for an AI model. This skill guides search method selection, source filtering, result interpretation, and troubleshooting empty or unexpected results.
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Analyze single-cell data with scvi-tools, using deep learning to combine datasets, correct differences between experiments, and transfer cell labels. Model guides and reusable scripts cover RNA, protein, chromatin accessibility, spatial data, and downstream analysis.
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Create realistic sample data for testing, development, and demos. Specify fields, record counts, value ranges, and business rules, then receive CSV, JSON, SQL INSERT statements, or a Python generator script with validation and usage instructions.
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Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.
中文简介正在准备中。当前显示英语简介。
Evaluate AI models and agents on Google Cloud using test datasets and scoring criteria. Generate simulated user scenarios, analyze failures, and compare results across changes to guide improvements to prompts and code.
中文简介正在准备中。当前显示英语简介。
Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.
中文简介正在准备中。当前显示英语简介。
Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.
中文简介正在准备中。当前显示英语简介。
Learn how GPT language models work by preparing text, training a small model, and generating samples. Covers CPU experiments, custom datasets, further training of pretrained GPT-2 models, and reproducing GPT-2 with multiple GPUs.
中文简介正在准备中。当前显示英语简介。
Build Apache Spark pipelines for large datasets, including transformations, aggregations, and streaming analysis. Get PySpark or Scala code, partitioning strategies for dividing data across workers, and configuration advice to investigate slow jobs and resource usage.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
Clean, combine, summarize, and reshape tabular data with pandas DataFrames, Python's table-like data structures. Produces transformation code and validation checks, including time series processing and memory-conscious approaches for large datasets.
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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.
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Build or review quality control workflows for single-cell and single-nucleus RNA sequencing data. Select filters from observed data, detect doublets, assign cell types, and create UMAP plots that summarize cell relationships, while recording analysis decisions and outputs.
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Query ProteomeXchange through its PROXI API to inspect datasets, libraries, proteins, peptide forms, and spectra. Receive concise summaries focused on a dataset or identifier, and save complete responses when more detail is needed.
中文简介正在准备中。当前显示英语简介。
Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.
中文简介正在准备中。当前显示英语简介。
Collect customer reviews for a Taobao or Tmall product using its item ID. Extract reviewer names, dates, purchased variants, review text, and photo URLs across review pages to prepare material for feedback analysis or review datasets.
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Calculate the count, sum, minimum, maximum, and average of supplied numbers or datasets, then present the results in a clear, structured format. Analysis results can also be saved to a file when requested.
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Prepare data availability statements for Nature-family submissions by mapping research datasets to access routes, planning repositories, drafting dataset citations, and checking metadata—the descriptive information needed to find and reuse data.
中文简介正在准备中。当前显示英语简介。