9de5b76336
* 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
44 lines
1.7 KiB
Markdown
44 lines
1.7 KiB
Markdown
# Finetune OpenGV's InternVL with Axolotl
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[InternVL 3.5](https://huggingface.co/OpenGVLab/InternVL3_5-8B-HF) is a family of powerful vision-language models supporting dynamic resolution and multi-image understanding by OpenGV. It features a ViT-style vision encoder and strong language model backbone for tasks like visual question answering, OCR, and scene text understanding.
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This guide shows how to fine-tune it with Axolotl.
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## Getting started
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1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
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2. Install `timm` for vision model support:
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```bash
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uv pip install timm==1.0.19
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```
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3. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
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4. Run the finetuning example:
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```bash
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axolotl train examples/internvl3_5/internvl3_5-8b-qlora.yml
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```
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This config uses about 8.21 GiB VRAM. Let us know how it goes. Happy finetuning! 🚀
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### Tips
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- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
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- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
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- The dataset format follows the multi-modal format as seen [here](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
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## Optimization Guides
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Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
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## Related Resources
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- [InternVL Paper](https://huggingface.co/papers/2508.18265)
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- [Axolotl Docs](https://docs.axolotl.ai)
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- [Axolotl Website](https://axolotl.ai)
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- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
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- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)
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