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unsloth

Plan and debug Unsloth fine-tuning workflows for LoRA, QLoRA, and full finetuning.

Updated May 24, 2026
Search Tags:#fine-tuning#reinforcement learning#lora#qlora#unsloth#llm training#vram optimization
One-click install
npx skills add https://github.com/anxiety135790/hermes-skills --skill unsloth-anxiety135790
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/anxiety135790/hermes-skills/tree/main/mlops/training/unsloth
Command: npx skills add https://github.com/anxiety135790/hermes-skills --skill unsloth-anxiety135790

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires unsloth, torch, transformers, trl, datasets, peft, and includes references (resource) components.

What problem does it solve?

This Skill helps you plan, debug, and operate Unsloth fine-tuning workflows without having to piece together scattered documentation, hardware constraints, and training settings by hand.

Core Features & Use Cases

  • Fine-tuning guidance: Get help with LoRA, QLoRA, full finetuning, and continued pretraining for LLMs.
  • Training and RL workflows: Apply Unsloth to supervised fine-tuning, GRPO-style reinforcement learning, and vision or multimodal training.
  • Deployment support: Prepare models for saving, exporting, and running in tools like Ollama, llama.cpp, and vLLM.
  • Use case: A developer wants to fine-tune a Qwen or Llama model on a modest GPU and needs the right setup, memory budget, and parameter choices.

Quick Start

Ask for a step-by-step Unsloth fine-tuning plan for your model, dataset, GPU, and target output format.

Frequently Asked Questions about unsloth

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune an LLM with QLoRA to fit within a limited VRAM budget?▼

Fine-tuning an LLM with QLoRA reduces VRAM usage by loading quantized base weights and applying low-rank adapters. This Skill provides step-by-step setup, memory budgeting, and parameter selection to train models like Llama or Qwen on modest GPUs efficiently.

What's the best way to set up GRPO-style reinforcement learning for LLMs?▼

Setting up GRPO-style reinforcement learning requires configuring TRL and Unsloth for reward-driven training. This Skill guides you through the training workflow, debugging, and evaluation to successfully apply RL techniques to your language models.

Does Unsloth work with vision and multimodal models for training?▼

Yes, Unsloth supports vision and multimodal training workflows alongside text and TTS use cases. This Skill helps you configure and debug training settings for these diverse model types within your existing PyTorch and transformers environment.

Can I export my fine-tuned model to run in Ollama, llama.cpp, or vLLM?▼

You can export fine-tuned models directly for deployment in Ollama, llama.cpp, and vLLM. This Skill covers the saving, exporting, and formatting requirements needed to transition your trained weights from local environments to hosted inference platforms.

Why does my LoRA fine-tuning run out of memory on my GPU?▼

LoRA fine-tuning memory exhaustion often stems from incorrect batch sizes or missing VRAM optimizations. This Skill assists with VRAM planning and parameter adjustments to prevent out-of-memory errors during supervised fine-tuning and continued pretraining.

Do I need to install PEFT and datasets separately before starting LLM training?▼

Yes, PEFT and datasets are required dependencies for managing adapter models and loading training data. This Skill operates within a stack that includes torch, transformers, and trl to execute the complete fine-tuning and reinforcement learning setup.

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