Instructions to use AQuarterMile/Writing-Model-Qwen-32B-thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AQuarterMile/Writing-Model-Qwen-32B-thinking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AQuarterMile/Writing-Model-Qwen-32B-thinking") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AQuarterMile/Writing-Model-Qwen-32B-thinking") model = AutoModelForCausalLM.from_pretrained("AQuarterMile/Writing-Model-Qwen-32B-thinking", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AQuarterMile/Writing-Model-Qwen-32B-thinking with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AQuarterMile/Writing-Model-Qwen-32B-thinking" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AQuarterMile/Writing-Model-Qwen-32B-thinking", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/AQuarterMile/Writing-Model-Qwen-32B-thinking
- SGLang
How to use AQuarterMile/Writing-Model-Qwen-32B-thinking with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "AQuarterMile/Writing-Model-Qwen-32B-thinking" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AQuarterMile/Writing-Model-Qwen-32B-thinking", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "AQuarterMile/Writing-Model-Qwen-32B-thinking" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AQuarterMile/Writing-Model-Qwen-32B-thinking", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use AQuarterMile/Writing-Model-Qwen-32B-thinking with Docker Model Runner:
docker model run hf.co/AQuarterMile/Writing-Model-Qwen-32B-thinking
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---
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license: apache-2.0
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base_model:
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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```
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---
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license: apache-2.0
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base_model:
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- writing
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language:
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- zho
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---
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# WritingBench-Critic-Model-Qwen-32B-thinking
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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/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) on a 12K SFT dataset for writing evaluation tasks.
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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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