Fine-Tuning Qwen3-VL

2025-12-29 · Source: DebuggerCafe · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

This article details the fine-tuning process for the Qwen3-VL 2B model for an image-to-HTML conversion task. It utilizes the Sketch2Code dataset, specifically focusing on direct screenshots and their corresponding HTML files, filtering out hand-drawn images. The fine-tuning employs Hugging Face Transformers and Unsloth, with the model loaded in 4-bit quantization and configured for LoRA with r=64 and lora_alpha=128. A key challenge addressed is managing long context lengths, as HTML ground truth files can range from 5,000 to 75,000 tokens, necessitating a filtration step to ensure samples do not exceed 20,000 combined image and HTML tokens to prevent incomplete generations. The model was trained for 500 steps on an RTX 3080 GPU with 10GB VRAM, leveraging 14GB of system RAM for gradient offloading, achieving a loss of 0.238.

Key takeaway

For AI Engineers working on multimodal tasks like image-to-code generation, you should prioritize meticulous dataset preparation and context length management when fine-tuning large vision-language models. Filtering samples that exceed the model's effective context window, even if the theoretical maximum is higher, is crucial to prevent truncated outputs and ensure the model learns complete generation patterns, thereby improving output quality and reliability.

Key insights

Fine-tuning Qwen3-VL for image-to-HTML requires careful dataset preparation and context length management.

Principles

• Filter long sequences to prevent incomplete model generations.
• LoRA fine-tuning can improve specific task performance.

Method

Fine-tune Qwen3-VL 2B using Hugging Face Transformers and Unsloth on a filtered Sketch2Code dataset, applying LoRA with r=64 and lora_alpha=128, and managing context length to prevent truncation.

In practice

• Use Unsloth for efficient Qwen3-VL fine-tuning.
• Pre-filter datasets to manage token limits.
• Monitor VRAM and system RAM for gradient offloading.

Topics

• Qwen3-VL
• Fine-tuning
• Image to HTML
• LoRA
• Unsloth

Best for: AI Engineer, Machine Learning Engineer, Deep Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by DebuggerCafe.