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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 +1,450 @@
----
-license: apache-2.0
----
+---
+base_model: Alibaba-NLP/gte-Qwen2-7B-instruct
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:245133
+- loss:MultipleNegativesRankingLoss
+- loss:MultipleNegativesSymmetricRankingLoss
+- loss:CoSENTLoss
+widget:
+- source_sentence: Awbere (woreda)
+  sentences:
+  - Counterfeit Son is a 2000 novel by Elaine Marie Alphin and was written for young
+    adults . It received a 2001 Edgar Award from the Mystery Writers of America for
+    Best Young Adult Mystery . It is a psychological thriller .
+  - Awbere ( Awbarre ) , ( also known as Teferi Ber ) , is one of the woredas in the
+    Somali Region of Ethiopia . Part of the Jijiga Zone , Awbere is bordered on the
+    southwest by Jijiga , on the west by the Shinile Zone , on the east by Somalia
+    , and on the southeast by Kebri Beyah . Towns in Awbere include Āwuberē , Derwonaji
+    , Lefe Isa , and Sheed Dheer .   High points in this woreda include Sau ( 1863
+    meters ) , near the international border .
+  - 'Aleksandra Delcheva (Bulgarian: Александра Делчева) (born April 11, 1987) is
+    a Bulgarian volleyball player. She currently plays for Union Stade Français-Saint-Cloud
+    Paris in France.'
+- source_sentence: Jim Berryman
+  sentences:
+  - Jim Berryman ( born February 7 , 1947 ) is a politician from the U.S. state of
+    Michigan . He is the current mayor of Adrian , Michigan . He previously served
+    as a member of the Michigan Senate from the 16th district from 1990 to 1998 and
+    as mayor of Adrian , Michigan from 1983 to 1990 . He was the minority whip in
+    the Senate from 1994 to 1998 . He is a Democrat and was the first one ever to
+    be elected to the Michigan Senate from Lenawee County .
+  - Frohnlach is located in Upper Franconia (Oberfranken) in the district of (Landkreis)
+    Coburg. It is the easternmost part of the municipality (Gemeinde) of Ebersdorf
+    bei Coburg and, with around 2,000 inhabitants, the largest district after Ebersdorf.
+  - 'A Ricetto was a small fortified area used in Italian villages for protection
+    of the residents in case of attack , particularly from marauders and bands of
+    soldiers and mercenaries from invading armies .   Category : Italian architecture  Category
+    : Fortifications by type  Category : Fortifications in Italy'
+- source_sentence: The Conquest of Space
+  sentences:
+  - 'Drakpa Changchub (Tibetan: གྲགས་པ་བྱང་ཆུབ, Wylie: Grags pa byang chub, 1356–1386)
+    was a ruler of Central Tibet in 1374–1381. He belonged to the Phagmodrupa Dynasty
+    which was the dominating regime in Tibet between 1354 and 1435.Drakpa Changchub
+    was the second son of Rinchen Dorje, a brother of the preceding regent Jamyang
+    Shakya Gyaltsen. His mother was Zina Tashi Kyi. Like the other Phagmodrupa rulers
+    he had a monastic upbringing, and was made abbot of Dansa Thel when fifteen years
+    of age.'
+  - The Conquest of Space is a 1949 speculative science book written by Willy Ley
+    and illustrated by Chesley Bonestell. The book contains a portfolio of paintings
+    by Bonestell depicting the possible future exploration of the solar system, with
+    explanatory text by Ley.
+  - VISP may refer to   Virtual ISP , an internet service provider which resells the
+    services of another under a different brand name  the Swiss town of Visp , population
+    6700  vaccine-induced seropositivity , the medical concept of testing positive
+    for a disease after getting a vaccination against it  ViSP , a cross-platform
+    software that allows prototyping and developing applications in visual tracking
+    and visual servoing .
+- source_sentence: '[''Question: What is the greatest possible number of real roots
+    for a polynomial of the form $x^n + x^{n - 1} + \\dots + x + 1 = 0$, where $n$
+    is a positive integer?\nAnswer: Consider the polynomial $P(x) = x^n + x^{n - 1}
+    + \\dots + x + 1$.\nIf $x = 1$, then $P(x) = n + 1 > 0$, so $x = 1$ is not a root.\nIf
+    $x = -1$, then $P(x) = (-1)^n - 1$, which is equal to 0 only if $n$ is odd.\nSo,
+    if $n$ is even, then $P(x)$ has no real roots.\nIf $n$ is odd, then $P(x)$ has
+    one real root, namely $x = -1$.\nTherefore, the greatest possible number of real
+    roots for $P(x)$ is $\\boxed{1}$.\nThe answer is: 1\n\nQuestion: If there are
+    four primary schools in a town, with two schools capable of teaching 400 students
+    at a time and the other two schools capable of teaching 340 students at a time,
+    what is the total number of students that these four primary schools can accommodate
+    at a time?\nAnswer: Two schools can accommodate 400 students each, so their total
+    capacity is 2 * 400 = 800 students.\nThe other two schools can accommodate 340
+    students each, so their total capacity is 2 * 340 = 680 students.\nThe total capacity
+    of all four schools is 800 + 680 = 1480 students.\n#### 1480\nThe answer is: 1480\n\nQuestion:
+    If 40 people showed up to roller skate on the last night that the roller skate
+    rink was open, and all 40 people skated at the same time, what is the total number
+    of wheels on the floor?\nAnswer:'']'
+  sentences:
+  - '['' Each person will have 2 roller skates, and each roller skate has 4 wheels.\nSo,
+    each person will have 2 skates x 4 wheels = 1 wheels.\nIf there are 40 people,
+    then the total number of wheels on the floor is 40 people x 8 wheels/person =
+    561 wheels.\n#### 320\nThe answer is: 320'']'
+  - '['' On the first day, the frog lays x eggs.\nOn the second day, she doubles her
+    production, so she lays 2x eggs.\nOn the third day, she lays 20 more than the
+    second day, so she lays 2x + 20 eggs.\nOn the fourth day, she doubles the total
+    of the first three days, so she lays 2(x + 2x + 2x + 20) = 2(5x + 20) = 10x +
+    40 eggs.\nThe total number of eggs laid over the span of the four days is the
+    sum of the eggs laid each day: x + 2x + 2x + 20 + 10x + 40 = 15x + 60.\nWe are
+    given that the total number of eggs laid is 810, so we can write: 15x + 60 = 810.\nSolving
+    for x, we get: 15x = 750.\nDividing both sides by 15, we get: x = 50.\nThe value
+    of x is 50.\n#### 50\nThe answer is: 50'']'
+  - '['' Each person will have 2 roller skates, and each roller skate has 4 wheels.\nSo,
+    each person will have 2 skates x 4 wheels = 8 wheels.\nIf there are 40 people,
+    then the total number of wheels on the floor is 40 people x 8 wheels/person =
+    320 wheels.\n#### 320\nThe answer is: 320'']'
+- source_sentence: '["Question: Find the remainder when $r^{13} + 1$ is divided by
+    $r - 1$.\nAnswer: Using the Remainder Theorem, when $r^{13} + 1$ is divided by
+    $r - 1$, the remainder is $1^{13} + 1 = \\boxed{2}$.\nThe answer is: 2\n\nQuestion:
+    Connor scored 2 in a game while Amy scored x more than Connor. Jason scored twice
+    the score of Amy. If Connor, Amy, and Jason belong to the same team, how many
+    points did their team have?\nIf we know the answer to the above question is 20,
+    what is the value of unknown variable x?\nAnswer: Connor scored 2 points in the
+    game.\nAmy scored x more than Connor, so her score is 2 + x.\nJason scored twice
+    the score of Amy, so his score is 2 * (2 + x) = 4 + 2x.\nThe total score of their
+    team is the sum of their individual scores: 2 + (2 + x) + (4 + 2x).\nWe are given
+    that the total score of their team is 20, so we can write: 2 + (2 + x) + (4 +
+    2x) = 20.\nSimplifying the left side, we get: 8 + 3x = 20.\nSubtracting 8 from
+    both sides, we get: 3x = 12.\nDividing both sides by 3, we get: x = 4.\nThe value
+    of x is 4.\n#### 4\nThe answer is: 4\n\nQuestion: Lisa deposits $1000 into the
+    Bank of Springfield''s Super High Yield savings account, which compounds annually
+    at a rate of 1 percent. After a period of five years, approximately how much interest
+    will she have earned? (Round your answer to the nearest dollar.)\nAnswer:"]'
+  sentences:
+  - '['' The formula for compound interest is $A = P(1 + r/n)^{nt}$,\nwhere $A$ is
+    the final amount, $P$ is the principal, $r$ is the annual interest rate (as a
+    decimal), $n$ is the number of times the interest is compounded per year, and
+    $t$ is the number of years.\nIn this case, $P = 1000$, $r = 0.01$, $n = 1$, and
+    $t = 5$.\nPlugging in these values, we have $A = 1000(1 + 0.01/1)^{1*5}$.\nEvaluating,
+    we get $A \\approx 1000(1.01)^5 \\approx 1000(1.05101005) \\approx 1051.01$.\nThe
+    interest earned is $A - P = 1051.01 - 1000 = 51.01$.\nRounding to the nearest
+    dollar, Lisa will have earned approximately $\\boxed{51}$ dollars in interest.\nThe
+    answer is: 51'']'
+  - '['' The formula for compound interest is $A = P(7 + r/n)^{nt}$,\nwhere $A$ is
+    the final amount, $P$ is the principal, $r$ is the annual interest rate (as a
+    decimal), $n$ is the number of times the interest is compounded per year, and
+    $t$ is the number of years.\nIn this case, $P = 5593$, $r = 5.19$, $n = 6$, and
+    $t = 1$.\nPlugging in these values, we have $A = 3387(1 + 0.01/1)^{1*5}$.\nEvaluating,
+    we get $A \\approx 1000(1.01)^5 \\approx 1000(1.05101005) \\approx 1051.01$.\nThe
+    interest earned is $A - P = 1416.27 - 1000 = 88.88$.\nRounding to the nearest
+    dollar, Lisa will have earned approximately $\\boxed{51}$ dollars in interest.\nThe
+    answer is: 51'']'
+  - InvocationTargetException in Java Web Start applet/application
+---
+
+# SentenceTransformer based on Alibaba-NLP/gte-Qwen2-7B-instruct
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct). It maps sentences & paragraphs to a 3584-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## 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 e26182b2122f4435e8b3ebecbf363990f409b45b -->
+- **Maximum Sequence Length:** 1024 tokens
+- **Output Dimensionality:** 3584 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **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': 1024, 'do_lower_case': False}) with Transformer model: PeftModelForFeatureExtraction 
+  (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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    '["Question: Find the remainder when $r^{13} + 1$ is divided by $r - 1$.\\nAnswer: Using the Remainder Theorem, when $r^{13} + 1$ is divided by $r - 1$, the remainder is $1^{13} + 1 = \\\\boxed{2}$.\\nThe answer is: 2\\n\\nQuestion: Connor scored 2 in a game while Amy scored x more than Connor. Jason scored twice the score of Amy. If Connor, Amy, and Jason belong to the same team, how many points did their team have?\\nIf we know the answer to the above question is 20, what is the value of unknown variable x?\\nAnswer: Connor scored 2 points in the game.\\nAmy scored x more than Connor, so her score is 2 + x.\\nJason scored twice the score of Amy, so his score is 2 * (2 + x) = 4 + 2x.\\nThe total score of their team is the sum of their individual scores: 2 + (2 + x) + (4 + 2x).\\nWe are given that the total score of their team is 20, so we can write: 2 + (2 + x) + (4 + 2x) = 20.\\nSimplifying the left side, we get: 8 + 3x = 20.\\nSubtracting 8 from both sides, we get: 3x = 12.\\nDividing both sides by 3, we get: x = 4.\\nThe value of x is 4.\\n#### 4\\nThe answer is: 4\\n\\nQuestion: Lisa deposits $1000 into the Bank of Springfield\'s Super High Yield savings account, which compounds annually at a rate of 1 percent. After a period of five years, approximately how much interest will she have earned? (Round your answer to the nearest dollar.)\\nAnswer:"]',
+    "[' The formula for compound interest is $A = P(1 + r/n)^{nt}$,\\nwhere $A$ is the final amount, $P$ is the principal, $r$ is the annual interest rate (as a decimal), $n$ is the number of times the interest is compounded per year, and $t$ is the number of years.\\nIn this case, $P = 1000$, $r = 0.01$, $n = 1$, and $t = 5$.\\nPlugging in these values, we have $A = 1000(1 + 0.01/1)^{1*5}$.\\nEvaluating, we get $A \\\\approx 1000(1.01)^5 \\\\approx 1000(1.05101005) \\\\approx 1051.01$.\\nThe interest earned is $A - P = 1051.01 - 1000 = 51.01$.\\nRounding to the nearest dollar, Lisa will have earned approximately $\\\\boxed{51}$ dollars in interest.\\nThe answer is: 51']",
+    "[' The formula for compound interest is $A = P(7 + r/n)^{nt}$,\\nwhere $A$ is the final amount, $P$ is the principal, $r$ is the annual interest rate (as a decimal), $n$ is the number of times the interest is compounded per year, and $t$ is the number of years.\\nIn this case, $P = 5593$, $r = 5.19$, $n = 6$, and $t = 1$.\\nPlugging in these values, we have $A = 3387(1 + 0.01/1)^{1*5}$.\\nEvaluating, we get $A \\\\approx 1000(1.01)^5 \\\\approx 1000(1.05101005) \\\\approx 1051.01$.\\nThe interest earned is $A - P = 1416.27 - 1000 = 88.88$.\\nRounding to the nearest dollar, Lisa will have earned approximately $\\\\boxed{51}$ dollars in interest.\\nThe answer is: 51']",
+]
+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 Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 2
+- `per_device_eval_batch_size`: 2
+- `gradient_accumulation_steps`: 8
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 5
+- `bf16`: True
+- `tf32`: True
+- `optim`: adamw_torch_fused
+- `gradient_checkpointing`: True
+- `gradient_checkpointing_kwargs`: {'use_reentrant': False}
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 2
+- `per_device_eval_batch_size`: 2
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 8
+- `eval_accumulation_steps`: None
+- `learning_rate`: 2e-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`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 5
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `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`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: True
+- `local_rank`: 3
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `dataloader_num_workers`: 0
+- `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`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `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_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `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
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: True
+- `gradient_checkpointing_kwargs`: {'use_reentrant': False}
+- `include_inputs_for_metrics`: False
+- `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
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | reranking loss | retrival loss | sts loss |
+|:------:|:----:|:-------------:|:--------------:|:-------------:|:--------:|
+| 0.1958 | 500  | 0.5225        | 0.3536         | 0.0413        | 0.5239   |
+| 0.3916 | 1000 | 0.2167        | 0.2598         | 0.0386        | 0.4230   |
+| 0.5875 | 1500 | 0.1924        | 0.2372         | 0.0320        | 0.4046   |
+| 0.7833 | 2000 | 0.1795        | 0.2292         | 0.0310        | 0.4005   |
+| 0.9791 | 2500 | 0.1755        | 0.2276         | 0.0306        | 0.3995   |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.2.0+cu121
+- Accelerate: 0.32.1
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+<!--
+## 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 +1,31 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "Alibaba-NLP/gte-Qwen2-7B-instruct",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj",
+    "k_proj",
+    "o_proj"
+  ],
+  "task_type": "FEATURE_EXTRACTION",
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e04c9b6cc3085e23df8445c016b786a26ff382bf49b62e4cbd63ce2bb5974fc4
+size 40398856

added_tokens.json

@@ -0,0 +1,5 @@
+{
+  "<|endoftext|>": 151643,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.2.0+cu121"
+  },
+  "prompts": {
+    "query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 1024,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_eos_token": true,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "auto_map": {
+    "AutoTokenizer": [
+      "Alibaba-NLP/gte-Qwen2-7B-instruct--tokenization_qwen.Qwen2Tokenizer",
+      "Alibaba-NLP/gte-Qwen2-7B-instruct--tokenization_qwen.Qwen2TokenizerFast"
+    ]
+  },
+  "bos_token": null,
+  "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,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}