Browse skills

This skill trains large language models like Megatron using orchestra-research techniques for enhanced performance and customization.

Provides LLMs guidance and assistance for building AI and machine learning applications.

This skill trains large language models like Megatron, accelerating AI development and enabling powerful custom LLM creation for diverse applications.

Dynamically adjusts reinforcement learning parameters to accelerate training and improve performance in complex environments.

Generates synthetic training data tailored to your model's needs, improving performance without real-world examples.

Sundial-org's training-data-curation refines datasets by identifying biases, errors, and gaps for improved AI model performance.

Analyzes GPU utilization and identifies performance bottlenecks during AI model training and inference tasks.

Simulates realistic dart throwing physics within DartSim environments, enabling training and analysis of robotic manipulation.

Generates clear, concise internal documentation and training materials based on provided context and desired style.

This skill trains large language models like Megatron, accelerating AI development and enabling powerful custom LLM creation for diverse applications.

Analyzes user performance data to identify areas for improvement and suggest personalized training strategies.

Provides debugging guidance and assistance for software development workflows.

Identifies and suggests fixes for Reinforcement Learning Model training errors based on logs and metrics.

Analyzes training logs to identify patterns and areas for improvement, boosting performance and efficiency through data-driven insights.

Enables parallel experimentation using Git worktrees within LGBARN's backend for efficient model training and version control.

Simulates RL environments using UV Miles data for robust agent training and improved performance in realistic scenarios.

Uv Moe Training guides AI models to generate charming, cute (moe) aesthetics and styles for creative content generation.

Dynamically adjusts training parameters based on real-time performance data to maximize model accuracy and efficiency.

Provides LLMs guidance and assistance for software development workflows.

GBSOSS's search-skill retrieves relevant information from external sources based on user queries, expanding knowledge beyond its training data.

Grpo RL Training facilitates rapid reinforcement learning model development by automating and optimizing agent training processes for improved performance.