65 lines
2.3 KiB
Markdown
65 lines
2.3 KiB
Markdown
---
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base_model: Qwen/Qwen2.5-7B-Instruct
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library_name: transformers
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license: apache-2.0
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tags:
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- llama-factory
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- generated_from_trainer
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pipeline_tag: text-generation
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model-index:
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- name: WritingBench-Critic-Model-Qwen-7B
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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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# WritingBench-Critic-Model-Qwen-7B
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<p align="center">
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📃 <a href="https://arxiv.org/abs/2503.05244" target="_blank">[Paper]</a> • 🚀 <a href="https://github.com/X-PLUG/WritingBench" target="_blank">[Github Repo]</a> • 📏 <a href="https://huggingface.co/AQuarterMile/WritingBench-Critic-Model-Qwen-7B" target="_blank">[Critic Model]</a> • ✍️ <a href="https://huggingface.co/AQuarterMile/Writing-Model-Qwen-7B" target="_blank">[Writer-7B]</a> <a href="https://huggingface.co/AQuarterMile/Writing-Model-Qwen-32B-thinking" target="_blank">[Writer-32B]</a>
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</p>
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This model is fine-tuned from [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on a 50K SFT dataset for writing evaluation tasks.
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For each criterion, the evaluator independently assigns a score on a 10-point scale to a response, providing both a score and a justification.
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 7e-06
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- train_batch_size: 1
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 64
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- total_eval_batch_size: 64
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- optimizer: Use OptimizerNames.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: 3
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### Framework versions
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- Transformers 4.46.1
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- Pytorch 2.5.1+cu124
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- Datasets 3.1.0
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- Tokenizers 0.20.3
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## 📝 Citation
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```
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@misc{wu2025writingbench,
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title={WritingBench: A Comprehensive Benchmark for Generative Writing},
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author={Yuning Wu and Jiahao Mei and Ming Yan and Chenliang Li and Shaopeng Lai and Yuran Ren and Zijia Wang and Ji Zhang and Mengyue Wu and Qin Jin and Fei Huang},
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year={2025},
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url={https://arxiv.org/abs/2503.05244},
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}
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```
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