tags:
- diger
- rq-vae
- generative-recommendation
- semantic-id
- recommendation
pipeline_tag: feature-extraction
DIGER RQ-VAE Checkpoint (instruments)
This repository provides the pretrained RQ-VAE checkpoint for instruments used in the paper:
DIGER: Differentiable Semantic IDs for Generative Recommendation
Paper
This artifact is associated with the DIGER paper page:
https://huggingface.co/papers/2601.19711
Files
best_collision_model.pth: released checkpoint from the DIGER RQ-VAE pretraining pipeline.
Usage
Download with huggingface_hub:
from huggingface_hub import hf_hub_download
ckpt_path = hf_hub_download(
repo_id="junchenfu/diger-rqvae-instruments",
filename="best_collision_model.pth",
)
The checkpoint can be loaded with the RQ-VAE implementation in the DIGER GitHub repository:
https://github.com/junchen-fu/DIGER
Please refer to the repository README for the full training configuration and commands.
Embeddings
LLaMA embeddings used by DIGER should be generated following the procedure described in:
https://github.com/honghuibao2000/letter
Dataset Note
The underlying recommendation datasets are publicly available from their original sources. Processed interaction data are not hosted in this model repository; they can be regenerated following the DIGER codebase.
Citation
If you use this checkpoint, please cite the DIGER paper.