Instructions to use szang18/spatialvla-pickcube-50k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use szang18/spatialvla-pickcube-50k with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("IPEC-COMMUNITY/spatialvla-4b-224-pt") model = PeftModel.from_pretrained(base_model, "szang18/spatialvla-pickcube-50k") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from szang18/spatialvla-pickcube-50k: direct link, hf CLI and curl.
- Browser
- Download file 36.2 MB
-
https://huggingface.co/szang18/spatialvla-pickcube-50k/resolve/main/tokenizer.json
- Command line
-
hf download hf://szang18/spatialvla-pickcube-50k/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/szang18/spatialvla-pickcube-50k/resolve/main/tokenizer.json
36.2 MB
- Xet hash:
- c0bc5f7d73995be69ebca8d3b446d9d362d6de8fe3657527f5bb4be8a58175b6
- Size of remote file:
- 36.2 MB
- SHA256:
- 2523a63c898ebf0a32c7282a2e459ef2c950a846c5f3172305089e4149b6b6c3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.