{"enrichment":{"faq":[{"a":"gemma-trainer guides you through fine-tuning Gemma models on local hardware using memory-efficient methods like QLoRA and Unsloth. The skill covers supervised fine-tuning workflows that let you adapt Gemma on single GPUs, including hyperparameter configuration, training loops, and checkpoint management for practical consumer-grade setups.","q":"How do I fine-tune Gemma locally on consumer hardware?"},{"a":"gemma-trainer handles dataset preparation and validation for Gemma training workflows, including chat template formatting and structured data layouts. The skill guides you through preparing datasets in formats compatible with supervised fine-tuning, DPO alignment, and reward modeling pipelines, ensuring your data integrates smoothly with TRL and Unsloth frameworks.","q":"What dataset formats does gemma-trainer support for training?"},{"a":"gemma-trainer covers direct preference optimization (DPO) techniques for aligning Gemma model behavior without reward models. The skill walks through DPO training setup, preference pair formatting, and training loops to refine model outputs based on human preferences, complementing supervised fine-tuning for more nuanced behavioral control.","q":"How can I use DPO alignment with Gemma models?"},{"a":"gemma-trainer supports deployment workflows via GGUF conversion and LiteRT optimization for edge and mobile environments. The skill covers model format conversion, inference optimization, and device-specific configurations to run your fine-tuned Gemma models efficiently on resource-constrained platforms.","q":"How do I deploy trained Gemma models to mobile devices?"},{"a":"Yes, gemma-trainer includes guidance for multimodal training workflows extending Gemma to vision and audio modalities. The skill covers dataset preparation, training loops, and model adaptation techniques for multimodal fine-tuning, enabling you to extend Gemma's capabilities beyond text on local hardware.","q":"Does gemma-trainer support multimodal fine-tuning for vision and audio?"},{"a":"gemma-trainer leverages Unsloth and TRL for memory-efficient training, including QLoRA quantization and LoRA adapters. The skill provides hyperparameter tuning guidance and setup instructions to maximize training efficiency on limited VRAM, making Gemma fine-tuning accessible on consumer GPUs.","q":"What memory-efficient training methods does gemma-trainer use?"}],"shadow_tags":["parameter-efficient-tuning","on-device-deployment","preference-alignment","quantization-conversion","multimodal-adaptation","knowledge-distillation","memory-optimization","local-inference"],"summary_rewrite":"Train and adapt Gemma models on consumer hardware through supervised fine-tuning, direct preference optimization, and reward modeling workflows. The skill guides you through memory-efficient setups with Unsloth and TRL, dataset formatting, multimodal training for vision and audio, and deployment via GGUF or LiteRT."},"files":[{"bytes":7713,"path":"skills/gemma-trainer/SKILL.md","sha256":"fa063ee07e9aa80dfae2270c9cbf40dd0140a7fe7970ddf3e20f95a53d5b0a9b","url":"https://skillfed.io/files/google-gemma/gemma-skills/gemma-trainer/7c5a0542/SKILL.md"}],"id":"google-gemma/gemma-skills/gemma-trainer","links":{"html":"https://skillfed.io/google-gemma/gemma-skills/gemma-trainer","md":"https://skillfed.io/google-gemma/gemma-skills/gemma-trainer.md","repo":"https://github.com/google-gemma/gemma-skills"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":53,"language":"Python","last_updated":"2026-07-08","license":"Apache-2.0","name":"gemma-trainer","publisher":"google-gemma","stars":868},"relations":{"similar":[{"id":"google-gemma/gemma-skills/gemma-dev"},{"id":"synthetic-sciences/openscience/unsloth"},{"id":"synthetic-sciences/openscience/colab-finetuning"},{"id":"TYH-labs/unsloth-buddy/unsloth-buddy"},{"id":"duyet/codex-claude-plugins/unsloth-training"},{"id":"ScientiaCapital/skills/unsloth-training-skill"},{"id":"synthetic-sciences/openscience/trl-fine-tuning"},{"id":"Orchestra-Research/AI-Research-SKILLs/trl-fine-tuning"},{"id":"OpenLAIR/dr-claw/trl-fine-tuning"},{"id":"graniet/kheish/trl-fine-tuning"}]},"slug":{"owner":"google-gemma","repo":"gemma-skills","skill":"gemma-trainer"},"version":"7c5a0542"}