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* feat: move to uv first * fix: update doc to uv first * fix: merge dev/tests into uv pyproject * fix: update docker docs to match current config * fix: migrate examples to readme * fix: add llmcompressor to conflict * feat: rec uv sync with lockfile for dev/ci * fix: update docker docs to clarify how to use uv images * chore: docs * fix: use system python, no venv * fix: set backend cpu * fix: only set for installing pytorch step * fix: remove unsloth kernel and installs * fix: remove U in tests * fix: set backend in deps too * chore: test * chore: comments * fix: attempt to lock torch * fix: workaround torch cuda and not upgraded * fix: forgot to push * fix: missed source * fix: nightly upstream loralinear config * fix: nightly phi3 long rope not work * fix: forgot commit * fix: test phi3 template change * fix: no more requirements * fix: carry over changes from new requirements to pyproject * chore: remove lockfile per discussion * fix: set match-runtime * fix: remove unneeded hf hub buildtime * fix: duplicate cache delete on nightly * fix: torchvision being overridden * fix: migrate to uv images * fix: leftover from merge * fix: simplify base readme * fix: update assertion message to be clearer * chore: docs * fix: change fallback for cicd script * fix: match against main exactly * fix: peft 0.19.1 change * fix: e2e test * fix: ci * fix: e2e test
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 uv pip install --no-build-isolation 'axolotl[flash-attn]>=0.12.0' -
Install an extra dependency:
uv pip 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.