130ef7c51a
* fix to not use batch feature indexing * more vlm fixes * use AutoModelForImageTextToText * add example yaml and need num2words for chat template * improve handling of adding image tokens to conversation * add lfm2-vl support * update the lfm readme * fix markdown and add rtol for loss checks * feat: add smolvlm2 processing strat * fix: check for causal-conv1d in lfm models * feat: add docs for lfm2 * feat: add new models and tips to docs * feat: add smolvlm2 docs and remove extra dep * chore: update docs * feat: add video instructions * chore: cleanup * chore: comments * fix: typo * feat: add usage stats * chore: refactor --------- Co-authored-by: NanoCode012 <nano@axolotl.ai>
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.