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ZakariaAlMoktar
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Model Details
Model Provider
ZakariaAlMoktar
Model Tree
Qwen/Qwen3-Embedding-4B
Input Modalities
Output Modalities
Supported Functionality
| Property | Value |
|---|---|
| Base model | Qwen/Qwen3-Embedding-4B |
| Base revision | 5cf2132abc99cad020ac570b19d031efec650f2b |
| Released checkpoint | checkpoint-2000 |
| Adapter | LoRA r=16, alpha=32, dropout=0.05 |
| Target modules | q/k/v/o attention projections |
| Precision | bfloat16 |
| Training hardware | Single NVIDIA A40, 48 GB VRAM |
python
import torchfrom sentence_transformers import SentenceTransformermodel = SentenceTransformer("ZakariaAlMoktar/presto-qwen3-embedding-4b-arabic-ecommerce",device="cuda",model_kwargs={"dtype": torch.bfloat16},)instruction = ("Instruct: Given an Arabic or mixed-language e-commerce search query, ""retrieve all catalog products that match the requested product type, ""brand, model, variant, attributes, and constraints.\nQuery: ")query = model.encode([instruction + "قهوة عربية"], normalize_embeddings=True)products = model.encode(["قهوة عربية محمصة 500 جرام", "شاحن هاتف سريع"],normalize_embeddings=True,)scores = query @ products.T
Apply the instruction to queries only. Encode product titles without it.
The model is intended for candidate retrieval from short Arabic or Arabic-English product catalogs. Product availability, policy constraints, and final ranking should be handled by downstream application logic.
Queries were trained with up to four relevant products and four labeled hard negatives. Known equivalent positives were masked from the contrastive denominator. The complete objective and configuration are available in the Presto Arabic Search repository.
The adapter was developed for short e-commerce titles. Performance may differ for long documents, dialects absent from the training distribution, or catalogs with substantially different product naming conventions.
Apache-2.0. The adapter requires the Apache-2.0 Qwen base model.