'mode' 검색 결과

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.

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• Train a small model on Shakespeare using a CPU
• Prepare custom text for model training
• Further train a pretrained GPT-2 model

Convert language models to GGUF and reduce their size through quantization, which stores model weights at lower precision. Guides cover compression choices, local inference on CPUs and Apple Silicon, and integration with tools such as Ollama.

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• Running a language model on a CPU-only computer
• Choosing model compression for available memory
• Configuring Metal acceleration on Apple Silicon

Apply HQQ quantization, which stores language model weights using fewer bits without calibration examples. Covers precision settings, inference backends, saving compressed models, deployment with HuggingFace Transformers or vLLM, and further training with LoRA.

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• Compressing a model without calibration data
• Comparing 4-bit and 2-bit quantization settings
• Running quantized models with HuggingFace Transformers or vLLM

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.

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

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

Update copies of public-company Excel models using financial disclosures and mapped input cells. Deliver an updated workbook or review workbook, with source references, change logs, unresolved items, and implications for estimates and valuation where the model supports them.

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• Refresh a company model after earnings
• Map financial disclosures to model input cells
• Review changes and their source evidence

Create a Markdown threat model grounded in a codebase. Identify valuable assets, boundaries between trusted and untrusted components, realistic attack paths, and mitigations, then prioritize risks using explicit assumptions about likelihood and impact.

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• Map attack paths across a codebase
• Assess threats within a specific project directory
• Prioritize risks to data and credentials

Turn mathematical modeling reports into DrawIO diagrams showing solution steps, model structures, and data processing flows. Produce editable source files, export PDFs for papers when DrawIO is available, and record diagram choices and export results.

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• Illustrate the overall approach in a modeling paper
• Draw solution flows for individual subproblems
• Show model structure and variable relationships

Coordinates a mathematical modeling competition project from planning to final checks. It records formatting and language preferences, creates a plan and task checklist, and calls dedicated skills for modeling, coding, diagrams, paper writing, and verification.

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• Starting a mathematical modeling competition project
• Planning work around competition and formatting preferences
• Tracking progress across modeling and paper-writing stages

Implements mathematical models from a design report as reproducible code, runs calculations, and checks results. It records methods and validation findings in a results report and produces data-based PDF charts for papers.

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• Implementing models from a modeling report
• Checking constraints and evaluating prediction errors
• Creating PDF charts from computed results

Clear the model pin, a setting that holds a session to one model, in the current Codex session through Weave Router. The session continues within the same turn, and the router's response is reported.

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• Removing a model pin from the current Codex session
• Continuing the same turn after clearing a pin
• Using a short command to remove a model pin

Choose the AI model for each agent launch using saved preferences, session instructions, agent settings, and task type. Save global or per-agent choices for future sessions and include the selected model in launch acknowledgments.

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• Choose models automatically by task type
• Save a default model for all agents
• Assign a persistent model preference to one agent

Develop Bayesian models for quantitative social science, from choosing priors to reporting posterior results. Guide MCMC, a sampling method for estimating distributions, check convergence, and assess sensitivity for hierarchical models and small datasets.

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• Choosing priors for models with small datasets
• Implementing hierarchical models in Stan or PyMC
• Investigating MCMC convergence problems

Build a reproducible Stata analysis from data cleaning through modeling, diagnostic checks, and robustness tests. Generate runnable .do files, regression tables, and figures for empirical papers, with additional workflows for epidemiology and machine learning approaches to causal inference.

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• Building a reproducible Stata analysis pipeline
• Comparing regression specifications in a paper table
• Checking causal estimates with robustness tests

Upload model weights to Hugging Face Hub with an OpenMDW 1.1 license and a required research warning. Check the published README and license file after uploading or updating to confirm that the required information is present.

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• Publishing model weights to Hugging Face Hub
• Checking documentation after a model update
• Adding OpenMDW 1.1 license files and metadata

Forecast sales, sensor readings, and other single-variable time series with Google's TimesFM, without training a custom model. Accept CSV files, DataFrames, or arrays and obtain forecast values with prediction intervals that express uncertainty.

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• Forecast monthly sales from a CSV file
• Forecast demand across multiple stores
• Plot sensor forecasts with prediction intervals

Plan experiments, analyze data, and build and evaluate predictive models. The skill also covers explaining findings to stakeholders and designing production workflows for model deployment, monitoring, and retraining to support business decisions.

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• Designing experiments and A/B tests
• Preparing model inputs and evaluating predictions
• Planning model deployment and monitoring

Analyze a codebase to identify valuable assets, attack entry points, and realistic abuse paths. Produce a concise Markdown threat model that explains risk priorities, distinguishes existing protections from proposed mitigations, and makes assumptions explicit.

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• Map attack entry points in a codebase
• Assess threats within a specific project directory
• Prioritize risks to sensitive data and credentials

Build production systems for machine learning models, from deployment to monitoring and retraining. The skill also guides LLM integration and retrieval augmented generation, which uses retrieved information to support answers, with scripts and architecture references.

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• Deploying machine learning models to production
• Setting up model monitoring and retraining
• Building applications that answer using retrieved information

Develop healthcare prediction models from electronic health records and physiological signals. Use PyHealth to prepare data, train models for tasks such as mortality or readmission prediction, and evaluate accuracy, fairness, and how predictions can be explained.

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• Building mortality prediction models with MIMIC-IV
• Training and evaluating hospital readmission models
• Developing medication recommendation models with SafeDrug

Develop and review machine learning approaches through at least three rounds of critique and refinement. Compare models with simple reference predictions, question data assumptions, and investigate overfitting, feature choices, and evaluation methods before accepting a solution.

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• Comparing model choices with simple reference predictions
• Investigating gaps between training and test performance
• Reviewing feature choices and data leakage risks

Explain an existing machine learning model by summarizing influential input features and examining individual predictions. It helps investigate unexpected feature interactions and communicate model behavior to nontechnical stakeholders, while noting limitations and potentially misleading explanations.

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• Explaining why a model made a particular prediction
• Identifying influential input features
• Investigating unexpected feature interactions

Configure NeMo AutoModel training launches on a local node, a Slurm cluster scheduler, or cloud resources through SkyPilot. Prepare launch settings for GPUs, containers, environment variables, multi-node communication, and optional performance profiling.

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• Switching between local and cluster training launches
• Configuring a multi-node Slurm training job
• Preparing SkyPilot spot jobs with checkpoint recovery

Find Earth2Studio models, data sources, and examples for weather or climate tasks. Filters options by region, forecast type, and GPU memory, then checks whether data sources supply the variables required by each model using current documentation and source files.

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• Choosing weather models for a region and forecast horizon
• Comparing models against available GPU memory
• Checking model and data source variable compatibility