metadata
license: mit
datasets:
- seniichev/amazon-fashion-2023-full
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
zjkarina/omniRecsysLLM_semanticIDsmodality
Recommendation model with semantic IDs for Amazon Fashion.
Description
This model uses VQ-VAE to create semantic item IDs, enabling a more accurate understanding of semantic relationships between products.
Architecture
- Base model: Qwen2.5-Omni-7B
- Item vocabulary size: 709,036
- ID embedding dimension: 512
- VQ-VAE codebook size: 10,000
- VQ-VAE codebook dimension: 256
- Dataset: Amazon Fashion 2023 Full
Usage
from any2any_trainer.models.recommendation import SemanticIDRecommendationModel
# Load model
model = SemanticIDRecommendationModel.from_pretrained("zjkarina/omniRecsysLLM_semanticIDsmodality")
# Generate recommendations with semantic IDs
recommendations = model.predict_next_item(
text="The user bought jeans and a t-shirt",
id_ids=[12345, 67890], # Item IDs from purchase history
top_k=5,
use_semantic_ids=True
)
Training
The model was trained on the Amazon Fashion 2023 dataset using semantic IDs generated via VQ-VAE.