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colab-finetuning

Fine-tune large language models on Google Colab using LoRA, QLoRA, DPO, ORPO, and PPO.

Updated Jun 4, 2026
Search Tags:#fine-tuning#TRL#PEFT#large language models#Colab#unsloth#gradient checkpointing
One-click install
npx skills add https://github.com/kngender5/hermes --skill colab-finetuning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: colab-finetuning
Source: https://github.com/kngender5/hermes/tree/main/skills/mlops/colab-finetuning
Command: npx skills add https://github.com/kngender5/hermes --skill colab-finetuning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires unsloth, peft, trl, bitsandbytes, gradio, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of fine-tuning large language models on Google Colab, providing efficient and scalable parameter-efficient fine-tuning (PEFT) methods.

Core Features & Use Cases

  • Advanced PEFT Methods: Offers LoRA, QLoRA, DPO, ORPO, and PPO methods for fine-tuning.
  • Multi-GPU Support: Allows fine-tuning on multiple GPUs for increased speed and capacity.
  • Gradient Checkpointing: Implements gradient checkpointing for efficient memory usage.
  • Dataset Strategies: Provides guidance on dataset preparation for PEFT.
  • Evaluation: Offers evaluation methods for fine-tuned models.
  • Export Formats: Supports various export formats for fine-tuned models.
  • Use Case: Ideal for researchers and developers looking to fine-tune large language models on Colab with minimal VRAM and time.

Quick Start

Run the following command to fine-tune a model using QLoRA on Colab:

!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install peft trl bitsandbytes gradio -q

Frequently Asked Questions about colab-finetuning

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

FAQPage Schema
How do I fine-tune large language models on Google Colab without running out of VRAM?▼

To fine-tune large language models on Colab without exceeding VRAM limits, use parameter-efficient fine-tuning methods like QLoRA and enable gradient checkpointing to reduce memory consumption during training.

What is the difference between LoRA, DPO, ORPO, and PPO for PEFT?▼

LoRA adapts models by injecting low-rank matrices, while DPO, ORPO, and PPO are preference optimization methods that align model outputs with human preferences using different reward and loss strategies.

Can I use Unsloth and TRL together for multi-GPU training on Colab?▼

Yes, you can use Unsloth and TRL together for multi-GPU training on Colab to scale parameter-efficient fine-tuning, increasing training speed and accommodating larger models across available hardware.

How do I install Unsloth, PEFT, and TRL for QLoRA fine-tuning in a Colab notebook?▼

To install dependencies for QLoRA fine-tuning in Colab, run pip install for unsloth directly from GitHub, then install peft, trl, bitsandbytes, and gradio to enable the training environment.

What dataset strategies should I use for parameter-efficient fine-tuning with TRL?▼

Effective dataset strategies for parameter-efficient fine-tuning with TRL involve properly formatting input-output pairs for supervised training or preference pairs for DPO and ORPO alignment tasks.

What export formats are supported after fine-tuning a model with QLoRA and PEFT?▼

After fine-tuning with QLoRA and PEFT, the workflow supports various export formats for the adapted model, allowing deployment across different inference platforms and integration with Gradio interfaces.

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