axolotl/examples/smolvlm2/README.md

Finetune SmolVLM2 with Axolotl

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
   # Option A: manage dependencies in your project
   uv add 'axolotl>=0.12.0'
   uv pip install flash-attn --no-build-isolation
   
   # Option B: quick install
   uv pip install 'axolotl>=0.12.0'
   uv pip install flash-attn --no-build-isolation
   

2. Install an extra dependency:

   uv pip 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

• Multi-GPU Training
• LoRA Optimizations
• Multi-Node Training

Related Resources

• SmolVLM2 Blog
• Axolotl Docs
• Axolotl GitHub
• Axolotl Discord