# InternVL 3.5

InternVL 3.5 is a family of powerful vision-language models supporting dynamic resolution and multi-image understanding by OpenGV. It features a ViT-style vision encoder and strong language model backbone for tasks like visual question answering, OCR, and scene text understanding.

This guide shows how to fine-tune it with Axolotl.

## Getting started

1. Install Axolotl following the installation guide.

2. Install `timm` for vision model support:

       pip install timm==1.0.19

3. Install Cut Cross Entropy to reduce training VRAM usage.

4. Run the finetuning example:

       axolotl train examples/internvl3_5/internvl3_5-8b-qlora.yml

This config uses about 8.21 GiB VRAM. Let us know how it goes. Happy finetuning! 🚀

### Tips

- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
- Read more on how to load your own dataset at docs.
- The dataset format follows the multi-modal format as seen here.

## Optimization Guides

Please check the Optimizations doc.

## Related Resources

- InternVL Paper
- Axolotl Docs
- Axolotl Website
- Axolotl GitHub
- Axolotl Discord