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