Files

NanoCode012 9de5b76336 feat: move to uv first (#3545 )

* 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

2026-04-21 10:16:03 -04:00

internvl3_5-8b-qlora.yml

feat: add internvl3_5 (#3141 ) [skip-ci]

2025-12-25 18:07:59 +07:00

README.md

feat: move to uv first (#3545 )

2026-04-21 10:16:03 -04:00

README.md

Finetune OpenGV's InternVL with Axolotl

InternVL 3.5 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.

This guide shows how to fine-tune it with Axolotl.

Getting started

Install Axolotl following the installation guide.
Install timm for vision model support:
```
uv pip install timm==1.0.19
```
Install Cut Cross Entropy to reduce training VRAM usage.

Run the finetuning example:

axolotl train examples/internvl3_5/internvl3_5-8b-qlora.yml

This config uses about 8.21 GiB VRAM. Let us know how it goes. Happy finetuning! 🚀

Tips

You can run a full finetuning by removing the adapter: qlora and load_in_4bit: true from the config.
Read more on how to load your own dataset at docs.
The dataset format follows the multi-modal format as seen here.

Optimization Guides

Please check the Optimizations doc.

README.md

Finetune OpenGV's InternVL with Axolotl

Getting started

Tips

Optimization Guides

Related Resources