File size: 1,182 Bytes
007c064 4bfa0cb 007c064 90096b0 007c064 90096b0 007c064 90096b0 007c064 90096b0 007c064 90096b0 007c064 90096b0 007c064 90096b0 007c064 90096b0 007c064 90096b0 007c064 90096b0 007c064 90096b0 007c064 90096b0 007c064 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
---
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. |