LEARN, BUILD, APPLY

In v3.1.0 we added Qwen3-VL and made a promise: thanks to the new FAMILY_GROUPS registry, future model families would appear in the notebooks automatically, with no interface edits at all. Version 3.2.0 is that promise kept. InternVL3.5 — the latest generation of OpenGVLab’s InternVL line, released in August 2025 — is now the fourth family in the registry, and neither notebook changed by a single line to display it.

UVLM integrates the six Transformers-native -HF checkpoints, which run through the standard Transformers stack without any custom remote code. None of them is gated: no Hugging Face token required.

The registry now totals 21 checkpoints across 4 families, from 1B to 110B parameters — and the 1B entry replaces Qwen3-VL 2B as the smallest model UVLM has ever supported.

InternVL3.5 is not a variation on the Qwen conventions — it uses the standard Transformers pattern in which the chat template tokenizes directly (apply_chat_template(tokenize=True)), the generated tokens are sliced off after the prompt, and only the generated portion is decoded. That makes it the third distinct inference path in UVLM, alongside LLaVA’s string-based cleaning and Qwen’s separate vision preprocessing with token trimming. As always, all three converge at the same unified response parser — from the user’s side, InternVL3.5 is simply one more family in the dropdown.

Loading follows the same BF16-aware logic introduced in v3.1.0: BF16 automatically on GPUs with native support (RTX 30-series and newer, L4, A100), FP16 fallback otherwise.

While validating the new backend, we caught a fossil from UVLM’s two-backend era: the notebooks used a hardcoded rule that sent every non-Qwen2.5 model’s results to Score_Analysis_LLaVA.csv. With four families, that meant different models could silently append into the same CSV — and resume mode could not tell them apart. As of v3.2.0, output filenames are derived from the loaded checkpoint (e.g. Score_Analysis_InternVL3_5-8B-HF.csv), so each model writes its own file and resume mode and schema upgrades are per-model by construction. If you benchmark several models on the same image folder, this is the release that keeps your results honest.

Nothing changes in the workflow — install (or upgrade) and the new family is there:

pip install --upgrade --force-reinstall --no-deps
git+https://github.com/perezjoan/UVLM.git

Or open the Colab notebook — it always installs the latest version automatically. The three-block workflow (load → configure tasks → run batch), consensus validation, chain-of-thought mode, and truncation detection all work with InternVL3.5 out of the box. No dependency changes since v3.1.0. Tested locally on Windows 11 with an RTX 5060 laptop GPU, where the 1B model loads in about 15 seconds once cached.

One field note from validation, and a nice illustration of why UVLM separates format reliability from accuracy: under temperature sampling, InternVL3.5 1B answered a counting task with “There are two vehicles in the picture” — correct, but unparseable as an integer, so it was recorded as NA by design. Under greedy decoding with a strict format instruction, the same model returned a clean integer. Small models follow instructions best when you ask firmly and decode greedily.

The third family addition promised in v3.1.0 — the Gemma multimodal line — is coming next, and it will be a bigger step than a minor version: Gemma 4 requires the Transformers v5 line, which means UVLM’s next release will be a major version with a documented migration. Same discipline as always: one backend at a time, validated before released.

Full change log in VERSIONS.txt · Source and releases on GitHub · If you use UVLM in research, please cite our Software paper.