Instructions to use cabbagel/caT-VTG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cabbagel/caT-VTG with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
caT-VTG RLVR adapter, step 900
This is the exact complete checkpoint used by Team caT's final MARS2 2026 formal
inference run videochat3-whisper-inference-20260731-180133-9b3e95.
- Base model:
MCG-NJU/VideoChat3-4B - Training: direct-base RLVR, eight GPUs
- Checkpoint:
checkpoint-step-000900 - Adapter: LoRA rank 16, alpha 32, dropout 0.0
- Adapter weights SHA-256:
fac5e9b635067e3046a6b26362cd5cbf3fc07bc558daaca86964e35b5f917dbd - Complete checkpoint content SHA-256:
649abf427f52a0859f2eaae4ced5f34374384e52b4dfbb783f921ffc17b33ed1
The exact checkpoint is stored under checkpoint-step-000900/ so its original
README.md can remain part of the immutable content identity while this repository
keeps a separate Hugging Face model card. adapter_config.json and
adapter_model.safetensors in that directory are sufficient for the portable
reproduction runner in the companion GitHub repository. The optimizer, rank RNG,
training-state, completion marker, and checkpoint manifest files are included so
reviewers can also verify the original complete checkpoint identity.
The PEFT config is preserved byte-for-byte and therefore contains the absolute base
model path from the training host. The companion loader first materializes the base
model from MCG-NJU/VideoChat3-4B and then loads this adapter; it does not depend on
that historical host path. Point the reproduction command at
model/checkpoint-step-000900 after downloading this repository.
No official leaderboard score is claimed here. Use of the adapter is subject to the base model's license and the MARS2 competition data terms.
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Base model
MCG-NJU/VideoChat3-4B