# SmolVLM 2

SmolVLM2 are a family of lightweight, open-source multimodal models from HuggingFace designed to analyze and understand video, image, and text content.

These models are built for efficiency, making them well-suited for on-device applications where computational resources are limited. Models are available in multiple sizes, including 2.2B, 500M, and 256M.

This guide shows how to fine-tune SmolVLM2 models with Axolotl.

## Getting Started

1. Install Axolotl following the installation guide.

   Here is an example of how to install from pip:

       # Ensure you have a compatible version of Pytorch installed
       pip3 install packaging setuptools wheel ninja
       pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'

2. Install an extra dependency:

       pip3 install num2words==0.5.14

3. Run the finetuning example:

       # LoRA SFT (1x48GB @ 6.8GiB)
       axolotl train examples/smolvlm2/smolvlm2-2B-lora.yaml

## TIPS

- Dataset Format: For video finetuning, your dataset must be compatible with the multi-content Messages format. For more details, see our documentation on Multimodal Formats.
- Dataset Loading: Read more on how to prepare and load your own datasets in our documentation.

## Optimization Guides

Please check the Optimizations doc.

## Related Resources

- SmolVLM2 Blog
- Axolotl Docs
- Axolotl GitHub
- Axolotl Discord