Upload trained Bi-Encoder model & tokenizer at Step 500

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+    "embedding_dimension": 1024,
+    "pooling_mode": "lasttoken",
+    "include_prompt": true
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:49500
+- loss:MultipleNegativesRankingLoss
+base_model: prestoai/qwen3-embedding-0.6b-arabic-ecom
+widget:
+- source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the
+    product that best matches it
+
+    Query: عناية بالفم'
+  sentences:
+  - تن ريقا بزيت الزيتون 160جم
+  - Foramen Denture Clean Box
+  - صبغة شعر L'Oréal Paris - 5.45 Excellence
+- source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the
+    product that best matches it
+
+    Query: بسكويت شوكولاتة بالحليب'
+  sentences:
+  - بسكويت - Bahlsen
+  - حقيبة هدايا - RA040
+  - Cicabio Arnica+ - Bioderma
+- source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the
+    product that best matches it
+
+    Query: بيتر صودا'
+  sentences:
+  - ميرندا صودا
+  - زبدة فول السوداني حدائق كاليفورنيا ناعمه - 510 غ
+  - مشروب بيتر صودا - ميرندا
+- source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the
+    product that best matches it
+
+    Query: شيجلام بودرة'
+  sentences:
+  - بودرة SHEGLAM - High Coverage Linen
+  - برايمر فائق الترطيب - SHEGLAM
+  - سباتلة حجم صغير تريبولي سنتر
+- source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the
+    product that best matches it
+
+    Query: عطور نسائية'
+  sentences:
+  - تن الوفاء سكيب جاك بالزيت الزيتون - 160 غ
+  - مجموعة عطر نسائي - ابراهيم القرشي سكر
+  - قبعة رجالية - 07
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on prestoai/qwen3-embedding-0.6b-arabic-ecom
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [prestoai/qwen3-embedding-0.6b-arabic-ecom](https://huggingface.co/prestoai/qwen3-embedding-0.6b-arabic-ecom) on the pairs_with_negatives and positives datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [prestoai/qwen3-embedding-0.6b-arabic-ecom](https://huggingface.co/prestoai/qwen3-embedding-0.6b-arabic-ecom) <!-- at revision 80f273fd53c6644d65e14a2ac1fbf74b8c924097 -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Supported Modality:** Text
+- **Training Datasets:**
+    - pairs_with_negatives
+    - positives
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'Qwen3Model'})
+  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'lasttoken', 'include_prompt': True})
+  (2): Normalize({})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("leafxyz/main")
+# Run inference
+queries = [
+    'Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it\nQuery: عطور نسائية',
+]
+documents = [
+    'مجموعة عطر نسائي - ابراهيم القرشي سكر',
+    'قبعة رجالية - 07',
+    'تن الوفاء سكيب جاك بالزيت الزيتون - 160 غ',
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# [1, 1024] [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+# tensor([[ 0.5593, -0.0069,  0.0400]])
+```
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Datasets
+
+#### pairs_with_negatives
+
+* Dataset: pairs_with_negatives
+* Size: 9,900 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                          | negative                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 24 tokens</li><li>mean: 29.36 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.82 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 15.12 tokens</li><li>max: 44 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                              | positive                                     | negative                                     |
+  |:------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------|:---------------------------------------------|
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: زيت بابايا WKL</code>  | <code>زيت جسم - WKL Papaya</code>            | <code>زيت جسم - Vaseline Cocoa</code>        |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: منكير</code>           | <code>اظافر هيفا - TWINKLE</code>            | <code>اظافر هيفا - SPARKLE</code>            |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: توب فريش حفاضات</code> | <code>توب فريش حفاضات رقم 1 - 44 قطعة</code> | <code>توب فريش حفاضات رقم 2 - 40 قطعة</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+#### positives
+
+* Dataset: positives
+* Size: 39,600 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 23 tokens</li><li>mean: 29.52 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.77 tokens</li><li>max: 39 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                  | positive                                                 |
+  |:----------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------|
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: نبي فيكسول ارجواني</code>  | <code>منظف ​​الحمام الذكي فيكسول ارجواني - 900 مل</code> |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: شربة نجمة اريغي 500</code> | <code>شربة نجمة اريغي - 500 غ</code>                     |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: Gas relife drops</code>    | <code>Gas relife drops</code>                            |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+### Evaluation Datasets
+
+#### pairs_with_negatives
+
+* Dataset: pairs_with_negatives
+* Size: 100 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 100 samples:
+  |         | anchor                                                                             | positive                                                                          | negative                                                                         |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | string                                                                           |
+  | details | <ul><li>min: 24 tokens</li><li>mean: 29.35 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.71 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.2 tokens</li><li>max: 34 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                | positive                                                       | negative                                                   |
+  |:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|:-----------------------------------------------------------|
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: عناية بالجسم</code>      | <code>معطر جسم وشعر نسائي - Sol de Janeiro Água Mística</code> | <code>قارورة عصير - AS02</code>                            |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: بخاخ تشيكو 100 مل</code> | <code>بخاخ تشيكو للحماية من البعوض - 100 مل</code>             | <code>مناديل الحماية من البعوض تشيكو - 20 قطعة</code>      |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: بخاخ مانع التصاق</code>  | <code>بخاخ الطبخ بنكهة الفلفل مانع للالتصاق - 200 مل</code>    | <code>بخاخ الطبخ بنكهة الثوم مانع للالتصاق - 200 مل</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+#### positives
+
+* Dataset: positives
+* Size: 400 evaluation samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 400 samples:
+  |         | anchor                                                                             | positive                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 24 tokens</li><li>mean: 29.43 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.74 tokens</li><li>max: 35 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                              | positive                              |
+  |:------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------|
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: سوار نسائي ذهبي</code> | <code>سوار نسائي - DX052</code>       |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: سناكس</code>           | <code>شوكلاتة كندر ترونكي 8*48</code> |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: مشروب حليب</code>      | <code>حليب - Safi</code>              |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `gradient_accumulation_steps`: 4
+- `learning_rate`: 3e-05
+- `num_train_epochs`: 1
+- `warmup_steps`: 0.05
+- `fp16`: True
+- `dataloader_num_workers`: 2
+- `gradient_checkpointing`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `do_predict`: False
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `per_device_eval_batch_size`: 8
+- `gradient_accumulation_steps`: 4
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 3e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_ratio`: None
+- `warmup_steps`: 0.05
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `enable_jit_checkpoint`: False
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `use_cpu`: False
+- `seed`: 42
+- `data_seed`: None
+- `bf16`: False
+- `fp16`: True
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: -1
+- `ddp_backend`: None
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 2
+- `dataloader_prefetch_factor`: None
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: True
+- `gradient_checkpointing_kwargs`: None
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_num_input_tokens_seen`: no
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `use_cache`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | pairs with negatives loss | positives loss |
+|:------:|:----:|:-------------:|:-------------------------:|:--------------:|
+| 0.0323 | 50   | 0.2092        | -                         | -              |
+| 0.0646 | 100  | 0.2008        | -                         | -              |
+| 0.0970 | 150  | 0.1948        | -                         | -              |
+| 0.1293 | 200  | 0.1749        | -                         | -              |
+| 0.1616 | 250  | 0.1455        | -                         | -              |
+| 0.1939 | 300  | 0.1924        | -                         | -              |
+| 0.2262 | 350  | 0.1959        | -                         | -              |
+| 0.2586 | 400  | 0.1606        | -                         | -              |
+| 0.2909 | 450  | 0.1679        | -                         | -              |
+| 0.3232 | 500  | 0.1774        | 0.3304                    | 0.1113         |
+| 0.3555 | 550  | 0.1924        | -                         | -              |
+| 0.3878 | 600  | 0.1487        | -                         | -              |
+| 0.4202 | 650  | 0.1859        | -                         | -              |
+| 0.4525 | 700  | 0.1807        | -                         | -              |
+| 0.4848 | 750  | 0.1785        | -                         | -              |
+| 0.5171 | 800  | 0.1534        | -                         | -              |
+| 0.5495 | 850  | 0.1468        | -                         | -              |
+| 0.5818 | 900  | 0.1566        | -                         | -              |
+| 0.6141 | 950  | 0.1153        | -                         | -              |
+| 0.6464 | 1000 | 0.1322        | 0.3138                    | 0.0943         |
+| 0.6787 | 1050 | 0.1320        | -                         | -              |
+| 0.7111 | 1100 | 0.1533        | -                         | -              |
+| 0.7434 | 1150 | 0.1358        | -                         | -              |
+| 0.7757 | 1200 | 0.1457        | -                         | -              |
+| 0.8080 | 1250 | 0.1320        | -                         | -              |
+| 0.8403 | 1300 | 0.1680        | -                         | -              |
+| 0.8727 | 1350 | 0.1280        | -                         | -              |
+| 0.9050 | 1400 | 0.1632        | -                         | -              |
+| 0.9373 | 1450 | 0.1656        | -                         | -              |
+| 0.9696 | 1500 | 0.1363        | 0.3024                    | 0.0914         |
+
+
+### Training Time
+- **Training**: 2.0 hours
+
+### Framework Versions
+- Python: 3.12.13
+- Sentence Transformers: 5.4.1
+- Transformers: 5.0.0
+- PyTorch: 2.10.0+cu128
+- Accelerate: 1.13.0
+- Datasets: 5.0.0
+- Tokenizers: 0.22.2
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{oord2019representationlearningcontrastivepredictive,
+      title={Representation Learning with Contrastive Predictive Coding},
+      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
+      year={2019},
+      eprint={1807.03748},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1807.03748},
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

adapter_config.json

@@ -0,0 +1,48 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "prestoai/qwen3-embedding-0.6b-arabic-ecom",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "o_proj",
+    "up_proj",
+    "v_proj",
+    "k_proj",
+    "q_proj",
+    "gate_proj",
+    "down_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "FEATURE_EXTRACTION",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:395d5039dafc5027c4f825185c21ecbe03b7148fd77f135719947111e6d39c41
+size 40419816

chat_template.jinja

@@ -0,0 +1,85 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
+                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "pytorch": "2.10.0+cu128",
+    "sentence_transformers": "5.4.1",
+    "transformers": "5.0.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "document": "",
+    "query": "Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it\nQuery: "
+  },
+  "similarity_fn_name": "cosine"
+}

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,10 @@
+{
+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67b23115c7cace3b6700c333da5038e66e95a9c2c71531b6953563a8c8bb1dee
+size 11423967

tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 128,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}