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Qwen2-72b-4.25bpw-exl2

Text Generation
Transformers
Safetensors
English
qwen2
pretrained
conversational
text-generation-inference
Inference Endpoints
exl2
Model card Files Community
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  1. LICENSE +53 -0
  2. README.md +98 -0
  3. config.json +38 -0
  4. generation_config.json +7 -0
  5. merges.txt +0 -0
  6. model.safetensors.index.json +970 -0
  7. output-00001-of-00005.safetensors +3 -0
  8. output-00002-of-00005.safetensors +3 -0
  9. output-00003-of-00005.safetensors +3 -0
  10. output-00004-of-00005.safetensors +3 -0
  11. output-00005-of-00005.safetensors +3 -0
  12. tokenizer.json +0 -0
  13. tokenizer_config.json +40 -0
  14. vocab.json +0 -0
LICENSE ADDED
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+ Tongyi Qianwen LICENSE AGREEMENT
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+
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+ Tongyi Qianwen Release Date: August 3, 2023
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+
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+ By clicking to agree or by using or distributing any portion or element of the Tongyi Qianwen Materials, you will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
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+
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+ 1. Definitions
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+ a. This Tongyi Qianwen LICENSE AGREEMENT (this "Agreement") shall mean the terms and conditions for use, reproduction, distribution and modification of the Materials as defined by this Agreement.
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+ b. "We"(or "Us") shall mean Alibaba Cloud.
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+ c. "You" (or "Your") shall mean a natural person or legal entity exercising the rights granted by this Agreement and/or using the Materials for any purpose and in any field of use.
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+ d. "Third Parties" shall mean individuals or legal entities that are not under common control with Us or You.
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+ e. "Tongyi Qianwen" shall mean the large language models (including Qwen model and Qwen-Chat model), and software and algorithms, consisting of trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Us.
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+ f. "Materials" shall mean, collectively, Alibaba Cloud's proprietary Tongyi Qianwen and Documentation (and any portion thereof) made available under this Agreement.
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+ and conversions to other media types.
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+ 2. Grant of Rights
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+ 4. Restrictions
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+ If you are commercially using the Materials, and your product or service has more than 100 million monthly active users, You shall request a license from Us. You cannot exercise your rights under this Agreement without our express authorization.
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+
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+ 5. Rules of use
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+ a. The Materials may be subject to export controls or restrictions in China, the United States or other countries or regions. You shall comply with applicable laws and regulations in your use of the Materials.
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+ b. You can not use the Materials or any output therefrom to improve any other large language model (excluding Tongyi Qianwen or derivative works thereof).
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+
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+ 6. Intellectual Property
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+ a. We retain ownership of all intellectual property rights in and to the Materials and derivatives made by or for Us. Conditioned upon compliance with the terms and conditions of this Agreement, with respect to any derivative works and modifications of the Materials that are made by you, you are and will be the owner of such derivative works and modifications.
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+ 7. Disclaimer of Warranty and Limitation of Liability
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+ a. The term of this Agreement shall commence upon your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.
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+ b. We may terminate this Agreement if you breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, you must delete and cease use of the Materials. Sections 7 and 9 shall survive the termination of this Agreement.
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+ 9. Governing Law and Jurisdiction.
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+ a. This Agreement and any dispute arising out of or relating to it will be governed by the laws of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.
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+ b. The People's Courts in Hangzhou City shall have exclusive jurisdiction over any dispute arising out of this Agreement.
README.md ADDED
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+ ---
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+ license: other
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+ license_name: tongyi-qianwen
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+ license_link: https://huggingface.co/Qwen/Qwen2-72B/blob/main/LICENSE
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+ language:
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+ - en
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+ pipeline_tag: text-generation
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+ tags:
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+ - pretrained
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+ ---
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+
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+ # Qwen2-72B
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+
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+ ## Introduction
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+
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+ Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the 72B Qwen2 base language model.
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+
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+ Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.
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+
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+ For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/).
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+ <br>
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+
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+
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+ ## Model Details
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+ Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes.
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+
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+ ## Requirements
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+ The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
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+ ```
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+ KeyError: 'qwen2'
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+ ```
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+
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+
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+ ## Usage
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+
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+ We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.
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+
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+
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+ ## Performance
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+
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+ The evaluation of base models mainly focuses on the model performance of natural language understanding, general question answering, coding, mathematics, scientific knowledge, reasoning, multilingual capability, etc.
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+
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+ The datasets for evaluation include:
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+
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+ **English Tasks**: MMLU (5-shot), MMLU-Pro (5-shot), GPQA (5shot), Theorem QA (5-shot), BBH (3-shot), HellaSwag (10-shot), Winogrande (5-shot), TruthfulQA (0-shot), ARC-C (25-shot)
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+
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+ **Coding Tasks**: EvalPlus (0-shot) (HumanEval, MBPP, HumanEval+, MBPP+), MultiPL-E (0-shot) (Python, C++, JAVA, PHP, TypeScript, C#, Bash, JavaScript)
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+
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+ **Math Tasks**: GSM8K (4-shot), MATH (4-shot)
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+
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+ **Chinese Tasks**: C-Eval (5-shot), CMMLU (5-shot)
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+
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+ **Multilingual Tasks**: Multi-Exam (M3Exam 5-shot, IndoMMLU 3-shot, ruMMLU 5-shot, mMMLU 5-shot), Multi-Understanding (BELEBELE 5-shot, XCOPA 5-shot, XWinograd 5-shot, XStoryCloze 0-shot, PAWS-X 5-shot), Multi-Mathematics (MGSM 8-shot), Multi-Translation (Flores-101 5-shot)
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+
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+ #### Qwen2-72B performance
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+ | Datasets | DeepSeek-V2 | Mixtral-8x22B | Llama-3-70B | Qwen1.5-72B | Qwen1.5-110B | **Qwen2-72B** |
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+ | :--------| :---------: | :------------: | :------------: | :------------: | :------------: |:------------: |
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+ |Architecture | MoE | MoE | Dense | Dense | Dense | Dense |
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+ |#Activated Params | 21B | 39B | 70B | 72B | 110B | 72B |
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+ |#Params | 236B | 140B | 70B | 72B | 110B | 72B|
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+ | ***English*** | | | | | | |
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+ |MMLU |78.5 | 77.8 | 79.5 | 77.5 | 80.4 | **84.2** |
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+ |MMLU-Pro | - | 49.5 | 52.8 | 45.8 | 49.4 | **55.6** |
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+ |GPQA | -| 34.3 | 36.3 | 36.3 | 35.9 | **37.9** |
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+ |Theorem QA | -| 35.9 | 32.3 | 29.3 | 34.9 | **43.1** |
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+ |BBH | 78.9 |78.9 | 81.0 | 65.5 | 74.8 | **82.4** |
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+ |HellaSwag | 87.8 | **88.7** | 88.0 | 86.0 | 87.5 | 87.6 |
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+ |WindoGrande | 84.8|85.0 | **85.3** | 83.0 | 83.5 | 85.1 |
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+ |ARC-C | 70.0| **70.7** | 68.8 | 65.9 | 69.6 | 68.9 |
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+ |TruthfulQA | 42.2 | 51.0 | 45.6 | **59.6** | 49.6 | 54.8 |
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+ | ***Coding*** | | | | | | |
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+ |HumanEval | 45.7 | 46.3 | 48.2 | 46.3 | 54.3 | **64.6** |
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+ |MBPP |73.9 | 71.7 | 70.4 | 66.9 | 70.9 | **76.9** |
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+ |EvalPlus | 55.0 | 54.1 | 54.8 | 52.9 | 57.7 | **65.4** |
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+ |MultiPL-E |44.4 | 46.7 | 46.3 | 41.8 | 52.7 | **59.6** |
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+ | ***Mathematics*** | | | | | | |
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+ |GSM8K | 79.2 | 83.7 | 83.0 | 79.5 | 85.4 | **89.5** |
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+ |MATH | 43.6 | 41.7 | 42.5 | 34.1 | 49.6 | **51.1** |
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+ | ***Chinese*** | | | | | | |
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+ |C-Eval | 81.7 | 54.6 | 65.2 | 84.1 | 89.1 | **91.0** |
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+ |CMMLU | 84.0 | 53.4 | 67.2 | 83.5 | 88.3 | **90.1** |
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+ | ***Multilingual*** | | | | | | |
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+ |Mulit-Exam | 67.5 | 63.5 | 70.0 | 66.4 | 75.6 | **76.6** |
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+ |Multi-Understanding | 77.0 | 77.7 | 79.9 | 78.2 | 78.2 | **80.7** |
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+ |Multi-Mathematics | 58.8 | 62.9 | 67.1 | 61.7 | 64.4 | **76.0** |
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+ |Multi-Translation | 36.0 | 23.3 | **38.0** | 35.6 | 36.2 | 37.8 |
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+
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+ ## Citation
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+
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+ If you find our work helpful, feel free to give us a cite.
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+
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+ ```
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+ @article{qwen2,
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+ title={Qwen2 Technical Report},
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+ year={2024}
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+ }
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+ ```
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+
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+ {
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+ "hidden_size": 8192,
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+ "num_attention_heads": 64,
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+ "rope_theta": 1000000.0,
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+ "transformers_version": "4.40.1",
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+ "use_cache": true,
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+ "use_sliding_window": false,
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+ "vocab_size": 152064,
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+ "quantization_config": {
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+ "quant_method": "exl2",
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+ "version": "0.1.4",
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+ "bits": 4.25,
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+ "head_bits": 6,
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+ "calibration": {
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+ "rows": 100,
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+ "length": 2048,
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+ "dataset": "(default)"
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+ }
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+ }
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+ }
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+ {
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