Use when you need maximum quality from a smaller/cheaper model — generates training data from a teacher model and fine-tunes a student model weights. Common scenarios - distilling GPT-4 quality into a cheaper model, generating training data from a strong teacher to fine-tune a weak student, reducing inference costs by replacing an expensive model with a fine-tuned small one, or building a production model that is fast and cheap. Related - ai-fine-tuning, ai-cutting-costs, dspy-better-together. Also used for dspy.BootstrapFinetune, model distillation with DSPy, teacher-student training, fine-tune small model from GPT-4 outputs, reduce API costs with fine-tuning, generate training data then fine-tune, cheap model same quality, distill large model into small model, fine-tune Llama from GPT-4, production model training, move from API to self-hosted model.
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryGeneralStars0
About this skill
Use when you need maximum quality from a smaller/cheaper model — generates training data from a teacher model and fine-tunes a student model weights. Common scenarios - distilling GPT-4 quality into a cheaper model, generating training data from a strong teacher to fine-tune a weak student, reducing inference costs by replacing an expensive model with a fine-tuned small one, or building a production model that is fast and cheap. Related - ai-fine-tuning, ai-cutting-costs, dspy-better-together. Also used for dspy.BootstrapFinetune, model distillation with DSPy, teacher-student training, fine-tune small model from GPT-4 outputs, reduce API costs with fine-tuning, generate training data then fine-tune, cheap model same quality, distill large model into small model, fine-tune Llama from GPT-4, production model training, move from API to self-hosted model.
Quick start
Copy the install prompt. Use the green button above this README.
Send it to your agent. Paste it into your coding agent’s chat and send.
Review the setup. Your agent checks the skill’s instructions, requirements, and installation scope.
Use when you need maximum quality from a smaller/cheaper model — generates training data from a teacher model and fine-tunes a student model weights. Common scenarios - distilling GPT-4 quality into a cheaper model, generating training data from a strong teacher to fine-tune a weak student, reducing inference costs by replacing an expensive model with a fine-tuned small one, or building a production model that is fast and cheap. Related - ai-fine-tuning, ai-cutting-costs, dspy-better-together. Also used for dspy.BootstrapFinetune, model distillation with DSPy, teacher-student training, fine-tune small model from GPT-4 outputs, reduce API costs with fine-tuning, generate training data then fine-tune, cheap model same quality, distill large model into small model, fine-tune Llama from GPT-4, production model training, move from API to self-hosted model.
About this listing
Category
General
Reported license
Not specified
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI