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  ---
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- library_name: transformers
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  license: other
 
 
 
 
 
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  base_model: Qwen/Qwen2.5-1.5B-Instruct
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  tags:
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- - llama-factory
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- - full
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- - generated_from_trainer
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- model-index:
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- - name: Qwen2.5-1.5B-Instruct-all-new
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- results: []
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  ---
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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-
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- # Qwen2.5-1.5B-Instruct-all-new
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-
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- This model is a fine-tuned version of [/ssd1/models/Qwen2.5-1.5B-Instruct/](https://huggingface.co//ssd1/models/Qwen2.5-1.5B-Instruct/) on the papertrain dataset.
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- It achieves the following results on the evaluation set:
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- - Loss: 0.5940
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-
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- ## Model description
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-
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- More information needed
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-
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- ## Intended uses & limitations
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-
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- More information needed
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-
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- ## Training and evaluation data
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-
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- More information needed
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-
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- ## Training procedure
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-
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- ### Training hyperparameters
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-
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- The following hyperparameters were used during training:
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- - learning_rate: 1e-05
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- - train_batch_size: 1
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- - eval_batch_size: 1
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- - seed: 42
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- - distributed_type: multi-GPU
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- - num_devices: 4
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- - gradient_accumulation_steps: 4
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- - total_train_batch_size: 16
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- - total_eval_batch_size: 4
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- - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- - lr_scheduler_type: cosine
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- - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 2.0
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-
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- ### Training results
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-
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-
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-
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- ### Framework versions
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-
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- - Transformers 4.48.3
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- - Pytorch 2.5.1+cu124
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- - Datasets 3.1.0
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- - Tokenizers 0.21.0
 
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  ---
 
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  license: other
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+ license_name: mg0-2.0
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+ license_link: https://github.com/Xtra-Computing/ModelGo/blob/main/MG_licenses/V2/MG0-2.0.txt
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+ language:
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+ - en
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+ pipeline_tag: text-generation
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  base_model: Qwen/Qwen2.5-1.5B-Instruct
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  tags:
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+ - chat
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+ library_name: transformers
 
 
 
 
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  ---
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+ ```
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+ @misc{chen2025xtragpt,
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+ title={XtraGPT: LLMs for Human-AI Collaboration on Controllable Academic Paper Revision},
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+ author={Nuo Chen and Andre Lin HuiKai and Jiaying Wu and Junyi Hou and Zining Zhang and Qian Wang and Xidong Wang and Bingsheng He},
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+ year={2025},
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+ eprint={2505.11336},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL},
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+ url={https://arxiv.org/abs/2505.11336},
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+ }
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+ ```