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vla-finetuning

Fine-tune Vision-Language-Action models with LoRA and Q4 quantization.

Updated May 21, 2026
Search Tags:#fine-tuning#VLA models#OpenVLA#π0#Octo
One-click install
npx skills add https://github.com/KAIROS7-team3/Final_project --skill vla-finetuning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: vla-finetuning
Source: https://github.com/KAIROS7-team3/Final_project/tree/main/.claude/skills/vla-finetuning
Command: npx skills add https://github.com/KAIROS7-team3/Final_project --skill vla-finetuning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openvla, pali_gemma, transformer_scratch, di_t, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of fine-tuning Vision-Language-Action (VLA) models, offering a comprehensive guide to selecting, training, and evaluating these models for specific tasks.

Core Features & Use Cases

  • Model Selection: Compare and choose between OpenVLA, π0, and Octo models based on performance, size, and compatibility with hardware.
  • Fine-Tuning: Guide on setting up and fine-tuning VLA models with LoRA and Q4 quantization for efficient use of GPU resources.
  • Evaluation: Provide metrics and techniques for evaluating the performance of fine-tuned models.

Quick Start

Use the vla-finetuning skill to fine-tune the OpenVLA-7B model on your dataset using LoRA and Q4 quantization.

Frequently Asked Questions about vla-finetuning

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

FAQPage Schema
How do I fine-tune a Vision-Language-Action model with limited GPU memory?▼

Fine-tune Vision-Language-Action models using memory-efficient techniques like LoRA and Q4 quantization. This approach allows you to train models such as OpenVLA-7B on your dataset while significantly reducing GPU resource requirements.

What is the best way to compare OpenVLA, π0, and Octo models for my task?▼

Compare OpenVLA, π0, and Octo VLA models based on their performance, size, and hardware compatibility. This Skill provides guidance on evaluating these specific models to help you select the optimal architecture for your precision tasks.

Do I need prior knowledge of VLA models before starting fine-tuning?▼

Yes, fine-tuning VLA models requires existing knowledge of Vision-Language-Action architectures and training techniques. You should understand the fundamentals before applying memory-efficient methods like LoRA and Q4 quantization.

How do I evaluate the performance of fine-tuned VLA models?▼

Evaluate fine-tuned VLA models using provided metrics and evaluation techniques. This Skill includes guidance on measuring the performance of your trained OpenVLA, π0, or Octo models to ensure they meet your precision requirements.

Can I use Q4 quantization and LoRA together for VLA fine-tuning?▼

Yes, you can combine LoRA with Q4 quantization for VLA fine-tuning. Using both techniques simultaneously maximizes GPU memory efficiency, enabling the training of large models like OpenVLA-7B on constrained hardware.

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