Text Ranking
sentence-transformers
Safetensors
qwen2
cross-encoder
reranker
text-embeddings-inference
Instructions to use cross-encoder-testing/mxbai-rerank-large-v2-STv6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder-testing/mxbai-rerank-large-v2-STv6 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("cross-encoder-testing/mxbai-rerank-large-v2-STv6") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
Uploading CrossEncoder model.
Browse files- .gitattributes +1 -0
- 1_CausalScoreHead/config.json +4 -0
- README.md +138 -0
- added_tokens.json +24 -0
- chat_template.jinja +9 -0
- config.json +59 -0
- config_sentence_transformers.json +11 -0
- generation_config.json +14 -0
- merges.txt +0 -0
- model.safetensors +3 -0
- modules.json +14 -0
- sentence_bert_config.json +14 -0
- special_tokens_map.json +31 -0
- tokenizer.json +3 -0
- tokenizer_config.json +208 -0
- vocab.json +0 -0
.gitattributes
CHANGED
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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1_CausalScoreHead/config.json
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{
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"true_token_id": 16,
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"false_token_id": 15
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}
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README.md
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| 1 |
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---
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tags:
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- sentence-transformers
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- cross-encoder
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- reranker
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base_model: mixedbread-ai/mxbai-rerank-large-v2
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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---
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# CrossEncoder based on mixedbread-ai/mxbai-rerank-large-v2
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This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-rerank-large-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
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## Model Details
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### Model Description
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- **Model Type:** Cross Encoder
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- **Base model:** [mixedbread-ai/mxbai-rerank-large-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2) <!-- at revision 763adb305c5b6af647d386e8dd5d511a45cca766 -->
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- **Maximum Sequence Length:** 32768 tokens
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- **Number of Output Labels:** 1 label
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<!-- - **Training Dataset:** Unknown -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
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- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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| 45 |
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from sentence_transformers import CrossEncoder
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# Download from the 🤗 Hub
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| 48 |
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model = CrossEncoder("cross-encoder-testing/mxbai-rerank-large-v2-v6")
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| 49 |
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# Get scores for pairs of texts
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| 50 |
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pairs = [
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| 51 |
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['How many calories in an egg', 'There are on average between 55 and 80 calories in an egg depending on its size.'],
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['How many calories in an egg', 'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.'],
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['How many calories in an egg', 'Most of the calories in an egg come from the yellow yolk in the center.'],
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]
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scores = model.predict(pairs)
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| 56 |
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print(scores.shape)
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# (3,)
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# Or rank different texts based on similarity to a single text
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ranks = model.rank(
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'How many calories in an egg',
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[
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| 63 |
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'There are on average between 55 and 80 calories in an egg depending on its size.',
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| 64 |
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'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.',
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'Most of the calories in an egg come from the yellow yolk in the center.',
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]
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)
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# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
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```
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<!--
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### Direct Usage (Transformers)
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<details><summary>Click to see the direct usage in Transformers</summary>
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</details>
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-->
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<!--
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### Downstream Usage (Sentence Transformers)
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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</details>
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-->
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<!--
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### Out-of-Scope Use
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-->
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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## Training Details
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### Framework Versions
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- Python: 3.11.6
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- Sentence Transformers: 5.3.0.dev0
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- Transformers: 4.57.3
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- PyTorch: 2.9.1+cu126
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- Accelerate: 1.6.0
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- Datasets: 4.2.0
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| 116 |
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- Tokenizers: 0.22.1
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| 117 |
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## Citation
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### BibTeX
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<!--
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## Glossary
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*Clearly define terms in order to be accessible across audiences.*
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-->
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| 127 |
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<!--
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| 129 |
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## Model Card Authors
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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-->
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| 133 |
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<!--
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## Model Card Contact
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| 136 |
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| 137 |
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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| 138 |
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-->
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added_tokens.json
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{
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"</tool_call>": 151658,
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"<tool_call>": 151657,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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| 15 |
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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| 17 |
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"<|quad_end|>": 151651,
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| 18 |
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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+
"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.jinja
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<|im_start|>system
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You are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>
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| 3 |
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<|im_start|>user
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| 4 |
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query: {{ messages[1]["content"] }}
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| 5 |
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document: {{ messages[2]["content"] }}
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| 6 |
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You are a search relevance expert who evaluates how well documents match search queries. For each query-document pair, carefully analyze the semantic relationship between them, then provide your binary relevance judgment (0 for not relevant, 1 for relevant).
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| 7 |
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Relevance:<|im_end|>
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| 8 |
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<|im_start|>assistant
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config.json
ADDED
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{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Qwen2ForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_dropout": 0.0,
|
| 6 |
+
"bos_token_id": 151643,
|
| 7 |
+
"dtype": "bfloat16",
|
| 8 |
+
"eos_token_id": 151645,
|
| 9 |
+
"hidden_act": "silu",
|
| 10 |
+
"hidden_size": 1536,
|
| 11 |
+
"initializer_range": 0.02,
|
| 12 |
+
"intermediate_size": 8960,
|
| 13 |
+
"layer_types": [
|
| 14 |
+
"full_attention",
|
| 15 |
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"full_attention",
|
| 16 |
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"full_attention",
|
| 17 |
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"full_attention",
|
| 18 |
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"full_attention",
|
| 19 |
+
"full_attention",
|
| 20 |
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"full_attention",
|
| 21 |
+
"full_attention",
|
| 22 |
+
"full_attention",
|
| 23 |
+
"full_attention",
|
| 24 |
+
"full_attention",
|
| 25 |
+
"full_attention",
|
| 26 |
+
"full_attention",
|
| 27 |
+
"full_attention",
|
| 28 |
+
"full_attention",
|
| 29 |
+
"full_attention",
|
| 30 |
+
"full_attention",
|
| 31 |
+
"full_attention",
|
| 32 |
+
"full_attention",
|
| 33 |
+
"full_attention",
|
| 34 |
+
"full_attention",
|
| 35 |
+
"full_attention",
|
| 36 |
+
"full_attention",
|
| 37 |
+
"full_attention",
|
| 38 |
+
"full_attention",
|
| 39 |
+
"full_attention",
|
| 40 |
+
"full_attention",
|
| 41 |
+
"full_attention"
|
| 42 |
+
],
|
| 43 |
+
"max_position_embeddings": 32768,
|
| 44 |
+
"max_window_layers": 21,
|
| 45 |
+
"model_type": "qwen2",
|
| 46 |
+
"num_attention_heads": 12,
|
| 47 |
+
"num_hidden_layers": 28,
|
| 48 |
+
"num_key_value_heads": 2,
|
| 49 |
+
"pad_token_id": 151645,
|
| 50 |
+
"rms_norm_eps": 1e-06,
|
| 51 |
+
"rope_scaling": null,
|
| 52 |
+
"rope_theta": 1000000.0,
|
| 53 |
+
"sliding_window": null,
|
| 54 |
+
"tie_word_embeddings": true,
|
| 55 |
+
"transformers_version": "4.57.3",
|
| 56 |
+
"use_cache": true,
|
| 57 |
+
"use_sliding_window": false,
|
| 58 |
+
"vocab_size": 151936
|
| 59 |
+
}
|
config_sentence_transformers.json
ADDED
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{
|
| 2 |
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"model_type": "CrossEncoder",
|
| 3 |
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"__version__": {
|
| 4 |
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"sentence_transformers": "5.3.0.dev0",
|
| 5 |
+
"transformers": "4.57.3",
|
| 6 |
+
"pytorch": "2.9.1+cu126"
|
| 7 |
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|
| 8 |
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"prompts": {},
|
| 9 |
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"default_prompt_name": null,
|
| 10 |
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"activation_fn": "torch.nn.modules.linear.Identity"
|
| 11 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,14 @@
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|
|
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|
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|
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|
|
|
|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"bos_token_id": 151643,
|
| 3 |
+
"do_sample": true,
|
| 4 |
+
"eos_token_id": [
|
| 5 |
+
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|
| 6 |
+
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|
| 7 |
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],
|
| 8 |
+
"pad_token_id": 151643,
|
| 9 |
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"repetition_penalty": 1.1,
|
| 10 |
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"temperature": 0.7,
|
| 11 |
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"top_k": 20,
|
| 12 |
+
"top_p": 0.8,
|
| 13 |
+
"transformers_version": "4.57.3"
|
| 14 |
+
}
|
merges.txt
ADDED
|
The diff for this file is too large to render.
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|
|
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
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|
|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:09552a915eb3650ec59e5263c4d78def1df64fa310bb27ece6e50e798a572b04
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| 3 |
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size 3087467144
|
modules.json
ADDED
|
@@ -0,0 +1,14 @@
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|
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|
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|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
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"idx": 0,
|
| 4 |
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"name": "0",
|
| 5 |
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"path": "",
|
| 6 |
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"type": "sentence_transformers.base.models.Transformer"
|
| 7 |
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},
|
| 8 |
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{
|
| 9 |
+
"idx": 1,
|
| 10 |
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"name": "1",
|
| 11 |
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"path": "1_CausalScoreHead",
|
| 12 |
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"type": "sentence_transformers.cross_encoder.models.CausalScoreHead"
|
| 13 |
+
}
|
| 14 |
+
]
|
sentence_bert_config.json
ADDED
|
@@ -0,0 +1,14 @@
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|
|
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|
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|
| 1 |
+
{
|
| 2 |
+
"transformer_task": "text-generation",
|
| 3 |
+
"modality_config": {
|
| 4 |
+
"text": {
|
| 5 |
+
"method": "forward",
|
| 6 |
+
"method_output_name": "logits"
|
| 7 |
+
},
|
| 8 |
+
"message": {
|
| 9 |
+
"method": "forward",
|
| 10 |
+
"method_output_name": "logits"
|
| 11 |
+
}
|
| 12 |
+
},
|
| 13 |
+
"module_output_name": "causal_logits"
|
| 14 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,31 @@
|
|
|
|
|
|
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|
|
|
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|
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|
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|
| 1 |
+
{
|
| 2 |
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"additional_special_tokens": [
|
| 3 |
+
"<|im_start|>",
|
| 4 |
+
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|
| 5 |
+
"<|object_ref_start|>",
|
| 6 |
+
"<|object_ref_end|>",
|
| 7 |
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"<|box_start|>",
|
| 8 |
+
"<|box_end|>",
|
| 9 |
+
"<|quad_start|>",
|
| 10 |
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"<|quad_end|>",
|
| 11 |
+
"<|vision_start|>",
|
| 12 |
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|
| 13 |
+
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|
| 14 |
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|
| 15 |
+
"<|video_pad|>"
|
| 16 |
+
],
|
| 17 |
+
"eos_token": {
|
| 18 |
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"content": "<|im_end|>",
|
| 19 |
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"lstrip": false,
|
| 20 |
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"normalized": false,
|
| 21 |
+
"rstrip": false,
|
| 22 |
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"single_word": false
|
| 23 |
+
},
|
| 24 |
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"pad_token": {
|
| 25 |
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"content": "<|endoftext|>",
|
| 26 |
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"lstrip": false,
|
| 27 |
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"normalized": false,
|
| 28 |
+
"rstrip": false,
|
| 29 |
+
"single_word": false
|
| 30 |
+
}
|
| 31 |
+
}
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:09f587f754d6f1dfd3c95c5c71992b2ca5292eb4ff1ebeddd6afe837e6d1999f
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| 3 |
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size 11422163
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tokenizer_config.json
ADDED
|
@@ -0,0 +1,208 @@
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
|
|
|
|
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|
| 1 |
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{
|
| 2 |
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"add_bos_token": false,
|
| 3 |
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"add_prefix_space": false,
|
| 4 |
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"added_tokens_decoder": {
|
| 5 |
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"151643": {
|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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},
|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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"special": true
|
| 20 |
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},
|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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"special": true
|
| 28 |
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},
|
| 29 |
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"151646": {
|
| 30 |
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"content": "<|object_ref_start|>",
|
| 31 |
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"lstrip": false,
|
| 32 |
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"normalized": false,
|
| 33 |
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|
| 34 |
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|
| 35 |
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"special": true
|
| 36 |
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},
|
| 37 |
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"151647": {
|
| 38 |
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"content": "<|object_ref_end|>",
|
| 39 |
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"lstrip": false,
|
| 40 |
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|
| 41 |
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|
| 42 |
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|
| 43 |
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"special": true
|
| 44 |
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},
|
| 45 |
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"151648": {
|
| 46 |
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"content": "<|box_start|>",
|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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"special": true
|
| 52 |
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},
|
| 53 |
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"151649": {
|
| 54 |
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|
| 55 |
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|
| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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"special": true
|
| 60 |
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},
|
| 61 |
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"151650": {
|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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"special": true
|
| 68 |
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},
|
| 69 |
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"151651": {
|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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|
| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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|
| 79 |
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|
| 80 |
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|
| 81 |
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|
| 82 |
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|
| 83 |
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|
| 84 |
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|
| 85 |
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"151653": {
|
| 86 |
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|
| 87 |
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|
| 88 |
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|
| 89 |
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|
| 90 |
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|
| 91 |
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"special": true
|
| 92 |
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},
|
| 93 |
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|
| 94 |
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|
| 95 |
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|
| 96 |
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|
| 97 |
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|
| 98 |
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|
| 99 |
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|
| 100 |
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},
|
| 101 |
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"151655": {
|
| 102 |
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"content": "<|image_pad|>",
|
| 103 |
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|
| 104 |
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|
| 105 |
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|
| 106 |
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|
| 107 |
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|
| 108 |
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|
| 109 |
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|
| 110 |
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|
| 111 |
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|
| 112 |
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|
| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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|
| 117 |
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|
| 118 |
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|
| 119 |
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|
| 120 |
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|
| 121 |
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|
| 122 |
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|
| 123 |
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|
| 124 |
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|
| 125 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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|
| 129 |
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|
| 130 |
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|
| 131 |
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|
| 132 |
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|
| 133 |
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|
| 134 |
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|
| 135 |
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|
| 136 |
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|
| 137 |
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|
| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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|
| 142 |
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|
| 143 |
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|
| 144 |
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|
| 145 |
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|
| 146 |
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|
| 147 |
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|
| 148 |
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|
| 149 |
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|
| 150 |
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|
| 151 |
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|
| 152 |
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|
| 153 |
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|
| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
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|
| 159 |
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|
| 160 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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|
| 167 |
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|
| 168 |
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|
| 169 |
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|
| 170 |
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|
| 171 |
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|
| 172 |
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|
| 173 |
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|
| 174 |
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|
| 175 |
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|
| 176 |
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|
| 177 |
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|
| 178 |
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|
| 179 |
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|
| 180 |
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|
| 181 |
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|
| 182 |
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|
| 183 |
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|
| 184 |
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|
| 185 |
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|
| 186 |
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|
| 187 |
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|
| 188 |
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|
| 189 |
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|
| 190 |
+
"<|quad_end|>",
|
| 191 |
+
"<|vision_start|>",
|
| 192 |
+
"<|vision_end|>",
|
| 193 |
+
"<|vision_pad|>",
|
| 194 |
+
"<|image_pad|>",
|
| 195 |
+
"<|video_pad|>"
|
| 196 |
+
],
|
| 197 |
+
"bos_token": null,
|
| 198 |
+
"clean_up_tokenization_spaces": false,
|
| 199 |
+
"eos_token": "<|im_end|>",
|
| 200 |
+
"errors": "replace",
|
| 201 |
+
"extra_special_tokens": {},
|
| 202 |
+
"model_max_length": 32768,
|
| 203 |
+
"pad_token": "<|endoftext|>",
|
| 204 |
+
"padding_side": "left",
|
| 205 |
+
"split_special_tokens": false,
|
| 206 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 207 |
+
"unk_token": null
|
| 208 |
+
}
|
vocab.json
ADDED
|
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|
|