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hugging-face-model-trainer

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Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment.

When to use

• Fine-tune language models on cloud GPUs without local infrastructure
• Train with TRL methods (SFT, DPO, GRPO, etc.)
• Run training jobs on Hugging Face Jobs infrastructure
• Convert trained models to GGUF for local deployment (Ollama, LM Studio, llama.cpp)
• Ensure trained models are permanently saved to the Hub
• Use modern workflows with optimized defaults
• **Limited GPU memory** - Unsloth uses ~60% less VRAM
• **Speed matters** - Unsloth is ~2x faster

When not to use

• Use this skill only when the task clearly matches its upstream product or API scope.
• Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes.
• Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.

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