---
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

```python
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.