---
title: WritingBench-Critic-Model-Qwen-7B
canonical_url: "https://www.modelscope.cn/models/iic/WritingBench-Critic-Model-Qwen-7B"
md_url: "https://www.modelscope.cn/models/iic/WritingBench-Critic-Model-Qwen-7B.md"
repository: iic/WritingBench-Critic-Model-Qwen-7B
last_updated: 2025-05-16
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
base_model:
  - Qwen/Qwen2.5-7B-Instruct
base_model_relation: finetune
parameters: 7.6B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 739
stars: 0
tags:
  - llama-factory
  - generated_from_trainer
---

# WritingBench-Critic-Model-Qwen-7B

> WritingBench-Critic-Model-Qwen-7B - iic 在 ModelScope 开源的模型。WritingBench-Critic-Model-Qwen-7B

iic/WritingBench-Critic-Model-Qwen-7B 是 ModelScope 魔搭社区上的 7.6B 参数text-generation模型，采用 apache-2.0 许可，基于 Qwen/Qwen2.5-7B-Instruct 构建，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: iic/WritingBench-Critic-Model-Qwen-7B
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 7.6B
- **Base model**: Qwen/Qwen2.5-7B-Instruct
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: llama-factory, generated_from_trainer
- **Downloads**: 739
- **Stars**: 0
- **Last updated**: 2025-05-16

Source: https://www.modelscope.cn/models/iic/WritingBench-Critic-Model-Qwen-7B

---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# WritingBench-Critic-Model-Qwen-7B

<p align="center">
  📃 <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>
</p>

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.

For each criterion, the evaluator independently assigns a score on a 10-point scale to a response, providing both a score and a justification.


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Framework versions

- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3

## 📝 Citation

```
@misc{wu2025writingbench,
      title={WritingBench: A Comprehensive Benchmark for Generative Writing}, 
      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},
      year={2025},
      url={https://arxiv.org/abs/2503.05244}, 
}
```