Robotics
Transformers
Safetensors
English
qwen2_5_vl
image-text-to-text
fine-tuned
robotics-diffusion-transformer
RDT
rdt
RDT 2
Vision-Language-Action
Bimanual
Manipulation
Zero-shot
UMI
VLA
text-generation-inference
Instructions to use TULLUS/RDT2-VQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TULLUS/RDT2-VQ with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("TULLUS/RDT2-VQ") model = AutoModelForMultimodalLM.from_pretrained("TULLUS/RDT2-VQ", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from TULLUS/RDT2-VQ: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/TULLUS/RDT2-VQ/resolve/main/tokenizer.json
- Command line
-
hf download hf://TULLUS/RDT2-VQ/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/TULLUS/RDT2-VQ/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 4bd2f092eeec244c0448f13e31edd4b25e625568713e4155993a3dea90c7437a
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.