Upload folder using huggingface_hub

.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

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 3584,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": true,
+  "include_prompt": true
+}

README.md

@@ -1,3 +1,529 @@
----
-license: apache-2.0
----
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:124788
+- loss:CachedGISTEmbedLoss
+base_model: Alibaba-NLP/gte-Qwen2-7B-instruct
+widget:
+- source_sentence: 其他机械、设备和有形货物租赁服务代表
+  sentences:
+  - 其他机械和设备租赁服务工作人员
+  - 电子和电信设备及零部件物流经理
+  - 工业主厨
+- source_sentence: 公交车司机
+  sentences:
+  - 表演灯光设计师
+  - 乙烯基地板安装工
+  - 国际巴士司机
+- source_sentence: online communication manager
+  sentences:
+  - trades union official
+  - social media manager
+  - budget manager
+- source_sentence: Projektmanagerin
+  sentences:
+  - Projektmanager/Projektmanagerin
+  - Category-Manager
+  - Infanterist
+- source_sentence: Volksvertreter
+  sentences:
+  - Parlamentarier
+  - Oberbürgermeister
+  - Konsul
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# Job - Job matching finetuned Alibaba-NLP/gte-Qwen2-7B-instruct
+
+Best performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A.  Use it for job title matching
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [Alibaba-NLP/gte-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) <!-- at revision a8d08b36ada9cacfe34c4d6f80957772a025daf2 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 3584 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Datasets:**
+    - full_en
+    - full_de
+    - full_es
+    - full_zh
+    - mix
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: Qwen2Model 
+  (1): Pooling({'word_embedding_dimension': 3584, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, '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("pj-mathematician/JobGTE-7b-Lora")
+# Run inference
+sentences = [
+    'Volksvertreter',
+    'Parlamentarier',
+    'Oberbürgermeister',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 3584]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### 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
+<details><summary>full_en</summary>
+
+#### full_en
+
+* Dataset: full_en
+* Size: 28,880 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: 2 tokens</li><li>mean: 4.4 tokens</li><li>max: 9 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 4.42 tokens</li><li>max: 10 tokens</li></ul> |
+* Samples:
+  | anchor                                   | positive                                 |
+  |:-----------------------------------------|:-----------------------------------------|
+  | <code>air commodore</code>               | <code>flight lieutenant</code>           |
+  | <code>command and control officer</code> | <code>flight officer</code>              |
+  | <code>air commodore</code>               | <code>command and control officer</code> |
+* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.01, 'mini_batch_size': 64, 'margin_strategy': 'absolute', 'margin': 0.0}
+  ```
+</details>
+<details><summary>full_de</summary>
+
+#### full_de
+
+* Dataset: full_de
+* Size: 23,023 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: 2 tokens</li><li>mean: 9.11 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 9.41 tokens</li><li>max: 33 tokens</li></ul> |
+* Samples:
+  | anchor                            | positive                                             |
+  |:----------------------------------|:-----------------------------------------------------|
+  | <code>Staffelkommandantin</code>  | <code>Kommodore</code>                               |
+  | <code>Luftwaffenoffizierin</code> | <code>Luftwaffenoffizier/Luftwaffenoffizierin</code> |
+  | <code>Staffelkommandantin</code>  | <code>Luftwaffenoffizierin</code>                    |
+* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.01, 'mini_batch_size': 64, 'margin_strategy': 'absolute', 'margin': 0.0}
+  ```
+</details>
+<details><summary>full_es</summary>
+
+#### full_es
+
+* Dataset: full_es
+* Size: 20,724 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: 3 tokens</li><li>mean: 9.42 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.18 tokens</li><li>max: 35 tokens</li></ul> |
+* Samples:
+  | anchor                              | positive                                   |
+  |:------------------------------------|:-------------------------------------------|
+  | <code>jefe de escuadrón</code>      | <code>instructor</code>                    |
+  | <code>comandante de aeronave</code> | <code>instructor de simulador</code>       |
+  | <code>instructor</code>             | <code>oficial del Ejército del Aire</code> |
+* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.01, 'mini_batch_size': 64, 'margin_strategy': 'absolute', 'margin': 0.0}
+  ```
+</details>
+<details><summary>full_zh</summary>
+
+#### full_zh
+
+* Dataset: full_zh
+* Size: 30,401 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: 3 tokens</li><li>mean: 4.7 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.04 tokens</li><li>max: 19 tokens</li></ul> |
+* Samples:
+  | anchor            | positive             |
+  |:------------------|:---------------------|
+  | <code>技术总监</code> | <code>技术和运营总监</code> |
+  | <code>技术总监</code> | <code>技术主管</code>    |
+  | <code>技术总监</code> | <code>技术艺术总监</code>  |
+* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.01, 'mini_batch_size': 64, 'margin_strategy': 'absolute', 'margin': 0.0}
+  ```
+</details>
+<details><summary>mix</summary>
+
+#### mix
+
+* Dataset: mix
+* Size: 21,760 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: 1 tokens</li><li>mean: 4.98 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 1 tokens</li><li>mean: 7.22 tokens</li><li>max: 27 tokens</li></ul> |
+* Samples:
+  | anchor                                    | positive                                                        |
+  |:------------------------------------------|:----------------------------------------------------------------|
+  | <code>technical manager</code>            | <code>Technischer Direktor für Bühne, Film und Fernsehen</code> |
+  | <code>head of technical</code>            | <code>directora técnica</code>                                  |
+  | <code>head of technical department</code> | <code>技术艺术总监</code>                                             |
+* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.01, 'mini_batch_size': 64, 'margin_strategy': 'absolute', 'margin': 0.0}
+  ```
+</details>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `gradient_accumulation_steps`: 2
+- `num_train_epochs`: 2
+- `warmup_ratio`: 0.05
+- `log_on_each_node`: False
+- `fp16`: True
+- `dataloader_num_workers`: 4
+- `fsdp`: ['full_shard', 'auto_wrap']
+- `fsdp_config`: {'transformer_layer_cls_to_wrap': ['Qwen2DecoderLayer'], 'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `ddp_find_unused_parameters`: True
+- `gradient_checkpointing`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 2
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-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`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: False
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `dataloader_num_workers`: 4
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: ['full_shard', 'auto_wrap']
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'transformer_layer_cls_to_wrap': ['Qwen2DecoderLayer'], 'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: True
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: True
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0165 | 1    | 4.5178        |
+| 0.0331 | 2    | 3.8803        |
+| 0.0496 | 3    | 2.8882        |
+| 0.0661 | 4    | 4.5362        |
+| 0.0826 | 5    | 3.6406        |
+| 0.0992 | 6    | 3.5285        |
+| 0.1157 | 7    | 4.1398        |
+| 0.1322 | 8    | 4.1543        |
+| 0.1488 | 9    | 4.4487        |
+| 0.1653 | 10   | 4.7408        |
+| 0.1818 | 11   | 2.1874        |
+| 0.1983 | 12   | 3.3176        |
+| 0.2149 | 13   | 2.8286        |
+| 0.2314 | 14   | 2.87          |
+| 0.2479 | 15   | 2.4834        |
+| 0.2645 | 16   | 2.7856        |
+| 0.2810 | 17   | 3.1948        |
+| 0.2975 | 18   | 2.1755        |
+| 0.3140 | 19   | 1.9861        |
+| 0.3306 | 20   | 2.0536        |
+| 0.3471 | 21   | 2.7626        |
+| 0.3636 | 22   | 1.6489        |
+| 0.3802 | 23   | 2.078         |
+| 0.3967 | 24   | 1.5864        |
+| 0.4132 | 25   | 1.8815        |
+| 0.4298 | 26   | 1.8041        |
+| 0.4463 | 27   | 1.7482        |
+| 0.4628 | 28   | 1.191         |
+| 0.4793 | 29   | 1.4166        |
+| 0.4959 | 30   | 1.3215        |
+| 0.5124 | 31   | 1.2907        |
+| 0.5289 | 32   | 1.1294        |
+| 0.5455 | 33   | 1.1586        |
+| 0.5620 | 34   | 1.551         |
+| 0.5785 | 35   | 1.3628        |
+| 0.5950 | 36   | 0.9899        |
+| 0.6116 | 37   | 1.1846        |
+| 0.6281 | 38   | 1.2721        |
+| 0.6446 | 39   | 1.1261        |
+| 0.6612 | 40   | 0.9535        |
+| 0.6777 | 41   | 1.2086        |
+| 0.6942 | 42   | 0.7472        |
+| 0.7107 | 43   | 1.0324        |
+| 0.7273 | 44   | 1.0397        |
+| 0.7438 | 45   | 1.185         |
+| 0.7603 | 46   | 1.2112        |
+| 0.7769 | 47   | 0.84          |
+| 0.7934 | 48   | 0.9286        |
+| 0.8099 | 49   | 0.8689        |
+| 0.8264 | 50   | 0.9546        |
+| 0.8430 | 51   | 0.8283        |
+| 0.8595 | 52   | 0.757         |
+| 0.8760 | 53   | 0.9199        |
+| 0.8926 | 54   | 0.7404        |
+| 0.9091 | 55   | 1.0995        |
+| 0.9256 | 56   | 0.8231        |
+| 0.9421 | 57   | 0.6297        |
+| 0.9587 | 58   | 0.9869        |
+| 0.9752 | 59   | 0.9597        |
+| 0.9917 | 60   | 0.7025        |
+| 1.0    | 61   | 0.4866        |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 4.1.0
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- Datasets: 3.5.0
+- Tokenizers: 0.21.1
+
+## 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",
+}
+```
+
+<!--
+## 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

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+  "loftq_config": {},
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+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
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added_tokens.json

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+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644
+}

config_sentence_transformers.json

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+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
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+    "query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: "
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+  "similarity_fn_name": "cosine"
+}

modules.json

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+    "type": "sentence_transformers.models.Transformer"
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pytorch_model_fsdp.bin

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sentence_bert_config.json

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special_tokens_map.json

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+    "<|im_end|>"
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tokenizer.json

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tokenizer_config.json

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+  "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
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