{"enrichment":{"faq":[{"a":"Unsloth Training accelerates LLM fine-tuning through GRPO (reinforcement learning with reward functions) and SFT (supervised learning with input-output pairs). Start by installing Unsloth, then configure your model and dataset. Unsloth handles automatic packing for mixed-length datasets and optimizes memory usage, allowing you to train faster with reduced computational overhead compared to standard training frameworks.","q":"How to use Unsloth for model training?"},{"a":"Unsloth Training cuts VRAM usage by 60% with FP8 training while maintaining model quality. It accelerates training speed significantly through optimized kernels and automatic dataset packing. These improvements mean you can fine-tune larger models on consumer hardware or reduce cloud costs, making LLM training more accessible and efficient than traditional approaches.","q":"What performance improvements does Unsloth offer over standard training?"},{"a":"Unsloth Training setup involves installing the framework, loading your base model, preparing your dataset, and selecting between GRPO (for reinforcement learning) or SFT (for supervised fine-tuning). Configure FP8 training to maximize memory efficiency, enable automatic packing for variable-length sequences, and specify your output format\u2014GGUF, Ollama, or vLLM\u2014based on your deployment target.","q":"How do I set up Unsloth training with optimized configuration?"},{"a":"Yes, Unsloth Training implements memory-efficient fine-tuning through FP8 quantization, reducing VRAM usage by 60%, and automatic packing that optimizes batch processing of mixed-length datasets. These techniques allow you to fine-tune models on hardware with limited memory while maintaining training speed and model performance, making it ideal for resource-constrained environments.","q":"Can Unsloth handle memory-efficient model fine-tuning?"},{"a":"Unsloth Training supports vision model tuning alongside traditional LLM fine-tuning. For deployment, it enables export to multiple formats including GGUF, Ollama, and vLLM for server inference. Additionally, Unsloth integrates with ExecuTorch for mobile deployment, allowing you to run fine-tuned models efficiently on edge devices after training.","q":"Does Unsloth support vision models and mobile deployment?"},{"a":"Unsloth Training best practices include: choose GRPO for reinforcement learning tasks or SFT for supervised fine-tuning; enable FP8 training to reduce memory footprint; use automatic packing for datasets with variable sequence lengths; monitor training metrics to catch issues early; and select the appropriate export format (GGUF, Ollama, vLLM, or ExecuTorch) based on your deployment environment before finalizing your model.","q":"What are Unsloth training best practices?"}],"shadow_tags":["model-optimization","training-acceleration","memory-efficiency","llm-fine-tuning","performance-boost","inference-speed","quantization-framework","resource-optimization"],"summary_rewrite":"Unsloth Training accelerates LLM fine-tuning through GRPO (reinforcement learning with reward functions) and SFT (supervised learning with input-output pairs). It cuts VRAM usage by 60% with FP8 training, speeds up mixed-length datasets via automatic packing, and supports vision model tuning, mobile deployment via ExecuTorch, and export to GGUF, Ollama, and vLLM."},"gist":{"api_url":"https://skillfed.io/api/skills/duyet/codex-claude-plugins/unsloth-training.json","as_of":"2026-07-24","description":"Unsloth Training enables faster LLM fine-tuning with GRPO, SFT. 8 stars \u00b7 updated Jul 2026. npx skillfed install duyet/codex-claude-plugins/unsloth-training","install":{"manual":["git clone https://github.com/duyet/codex-claude-plugins","cp -r codex-claude-plugins ~/.claude/skills/unsloth-training"],"primary":"npx skillfed install duyet/codex-claude-plugins/unsloth-training","version":"5651e4c9"},"kind":"skill","mirror_url":"https://skillfed.io/duyet/codex-claude-plugins/unsloth-training.md","similar":[{"id":"ScientiaCapital/skills/unsloth-training-skill","name":"Unsloth Training Skill","publisher":"ScientiaCapital/skills","url":"https://skillfed.io/ScientiaCapital/skills/unsloth-training-skill"},{"id":"synthetic-sciences/openscience/unsloth","name":"unsloth-fine-tuning","publisher":"synthetic-sciences/openscience","url":"https://skillfed.io/synthetic-sciences/openscience/unsloth"},{"id":"Orchestra-Research/AI-Research-SKILLs/trl-fine-tuning","name":"fine-tuning-with-trl","publisher":"Orchestra-Research/AI-Research-SKILLs","url":"https://skillfed.io/Orchestra-Research/AI-Research-SKILLs/trl-fine-tuning"},{"id":"moltis-org/moltis/fine-tuning-with-trl","name":"fine-tuning-with-trl","publisher":"moltis-org/moltis","url":"https://skillfed.io/moltis-org/moltis/fine-tuning-with-trl"}],"title":"Unsloth Training by duyet: Learn how to use Unsloth \u2014 SkillFed","use":{"when":["Unsloth Training cuts VRAM usage by 60% with FP8 training while maintaining model quality.","Unsloth Training setup involves installing the framework, loading your base model, preparing your dataset."]},"what":{"lead":"Unsloth Training enables faster LLM fine-tuning with GRPO, SFT, and FP8 training modes for efficient model adaptation.","rest":"Unsloth Training accelerates LLM fine-tuning through GRPO (reinforcement learning with reward functions) and SFT (supervised learning with input-output pairs). It cuts VRAM usage by 60% with FP8 training, speeds up mixed-length datasets via automatic packing, and supports vision model tuning, mobile deployment via ExecuTorch, and export to GGUF, Ollama, and vLLM."}},"id":"duyet/codex-claude-plugins/unsloth-training","install":{"mode":"external","repo":"https://github.com/duyet/codex-claude-plugins"},"links":{"html":"https://skillfed.io/duyet/codex-claude-plugins/unsloth-training","md":"https://skillfed.io/duyet/codex-claude-plugins/unsloth-training.md","repo":"https://github.com/duyet/codex-claude-plugins"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":2,"language":"Python","last_updated":"2026-07-24","license":null,"name":"Unsloth Training","publisher":"duyet","stars":8},"relations":{"similar":[{"id":"ScientiaCapital/skills/unsloth-training-skill"},{"id":"synthetic-sciences/openscience/unsloth"},{"id":"OpenLAIR/dr-claw/grpo-rl-training"},{"id":"synthetic-sciences/openscience/grpo-rl-training"},{"id":"Orchestra-Research/AI-Research-SKILLs/grpo-rl-training"},{"id":"graniet/kheish/grpo-rl-training"},{"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"}]},"slug":{"owner":"duyet","repo":"codex-claude-plugins","skill":"unsloth-training"},"version":"5651e4c9"}