{"enrichment":{"faq":[{"a":"Unsloth-fine-tuning accelerates LLM fine-tuning through optimized LoRA and QLoRA implementations that reduce both speed and memory requirements dramatically. By using low-rank adapters and quantization techniques, it enables training on consumer and datacenter GPUs with significantly lower VRAM footprint than standard approaches, making large model adaptation accessible on modest hardware.","q":"How does unsloth-fine-tuning achieve fast LLM fine-tuning with less VRAM?"},{"a":"Unsloth-fine-tuning supports reinforcement learning with GRPO and other RL algorithms for training reasoning models, supervised fine-tuning with chat templates and instruction tuning, vision and TTS model adaptation on single GPUs, and hyperparameter optimization for memory and training efficiency across 300+ model architectures including Llama, Qwen, and Gemma.","q":"What training methods does unsloth-fine-tuning support beyond standard supervised fine-tuning?"},{"a":"Yes, unsloth-fine-tuning is designed to fine-tune vision language models and text-to-speech models on single GPU setups. Its memory-efficient architecture and gradient checkpointing support enable training of these multimodal and specialized models without requiring expensive multi-GPU infrastructure.","q":"Can unsloth-fine-tuning train vision and TTS models on a single GPU?"},{"a":"Unsloth-fine-tuning exports trained models directly to GGUF format for deployment on Ollama, vLLM, and llama.cpp. You can merge LoRA adapters into base models and export the combined weights, enabling fast inference and easy deployment across popular inference frameworks.","q":"How do I export and deploy models trained with unsloth-fine-tuning?"},{"a":"Unsloth-fine-tuning delivers 2\u20135x faster model training compared to standard approaches while cutting VRAM requirements substantially through optimized LoRA/QLoRA implementations and gradient checkpointing. Exact savings depend on model size, batch configuration, and hardware, but the framework is engineered to maximize efficiency on both consumer and datacenter GPUs.","q":"What is the typical speedup and memory savings from unsloth-fine-tuning?"},{"a":"Unsloth-fine-tuning supports 300+ model architectures including Llama, Qwen, Gemma, and other popular LLMs, vision language models, and TTS systems. It handles models from 7B to larger scales with 4-bit quantization, LoRA, and QLoRA training modes, making it adaptable to a wide range of model sizes and types.","q":"Which model families and sizes does unsloth-fine-tuning work with?"}],"shadow_tags":["parameter-efficient-training","gpu-memory-optimization","reinforcement-learning-rl","vision-language-models","model-quantization","inference-acceleration","distributed-training","adapter-management","speech-synthesis","model-deployment"],"summary_rewrite":"Unsloth accelerates LLM fine-tuning on consumer and datacenter GPUs through optimized LoRA and QLoRA training, cutting both speed and memory requirements dramatically. It handles supervised fine-tuning, reinforcement learning with GRPO, vision model adaptation, and TTS training across 300+ model architectures, with direct export to GGUF for deployment on Ollama and llama.cpp."},"files":[{"bytes":26509,"path":"backend/cli/skills/ml-training/unsloth/SKILL.md","sha256":"b0f867c12ab3adf2b26f53a2b24c37002075bc1bdc5f512dde938a9bd16d2655","url":"https://skillfed.io/files/synthetic-sciences/openscience/unsloth/a6201e6d/SKILL.md"}],"id":"synthetic-sciences/openscience/unsloth","links":{"html":"https://skillfed.io/synthetic-sciences/openscience/unsloth","md":"https://skillfed.io/synthetic-sciences/openscience/unsloth.md","repo":"https://github.com/synthetic-sciences/openscience"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":403,"language":"TypeScript","last_updated":"2026-07-27","license":"Apache-2.0","name":"unsloth-fine-tuning","publisher":"synthetic-sciences","stars":2896},"relations":{"similar":[{"id":"TYH-labs/unsloth-buddy/unsloth-buddy"},{"id":"duyet/codex-claude-plugins/unsloth-training"},{"id":"ScientiaCapital/skills/unsloth-training-skill"},{"id":"wshobson/agents/finetuning-method-selection"},{"id":"synthetic-sciences/openscience/trl-fine-tuning"},{"id":"Orchestra-Research/AI-Research-SKILLs/trl-fine-tuning"},{"id":"OpenLAIR/dr-claw/trl-fine-tuning"},{"id":"moltis-org/moltis/fine-tuning-with-trl"},{"id":"graniet/kheish/trl-fine-tuning"},{"id":"NousResearch/hermes-agent/trl-fine-tuning"}]},"slug":{"owner":"synthetic-sciences","repo":"openscience","skill":"unsloth"},"version":"a6201e6d"}