| --- |
| 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`: |
|
|
| ```python |
| 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. |
|
|
