006f226270
* feat: update cce to include olmo family * chore: update docs following feedback * feat: add olmo3 config * fix: clarify 3 methods * chore: add olmo to readme
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
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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' -
Install an extra dependency:
pip3 install num2words==0.5.14 -
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.