Zhi-writing-dsr1-14b
1. Introduction
Zhi-writing-dsr1-14b is a fine-tuned model based on DeepSeek-R1-Distill-Qwen-14B, specifically optimized for enhanced creative writing capabilities. Several benchmark evaluations indicate the model's improved creative writing performance.
In the LLM Creative Story-Writing Benchmark, the model achieved a score of 8.33 compared to its base model's 7.8. In the WritingBench evaluation framework, it scored 8.46, showing improvement over DeepSeek-R1-Distill-Qwen-14B's 7.93. The model was also evaluated using GPT-4o on the AlpacaEval dataset, achieving an 82.6% win rate when compared with the base model.
The figure below shows the performance comparison across different domains in WritingBench:
2. Training Process
Data
The model's training corpus comprises three primary data sources: rigorously filtered open-source datasets, chain-of-thought reasoning corpora, and curated question-answer pairs from Zhihu.
To achieve optimal domain coverage, we meticulously balanced the distribution of various datasets, including Dolphin-r1, Congliu/Chinese-DeepSeek-R1-Distill-data-110k, OpenThoughts-114k, Light-R1-SFTData, and Light-R1-DPOData, alongside high-quality content from Zhihu. All datasets underwent comprehensive quality assurance through our Reward Model (RM) filtering pipeline.
Training
Supervised Fine-tuning (SFT): We employed a curriculum learning strategy for supervised fine-tuning. This methodical approach systematically enhances creative writing capabilities while incorporating diverse domain data to maintain core competencies and mitigate catastrophic forgetting.
Direct Preference Optimization (DPO): For scenarios involving minimal edit distances, we utilized Step-DPO (arxiv:2406.18629) to selectively penalize incorrect tokens, while incorporating positive constraints in the loss function as proposed in DPOP (arXiv:2402.13228).
3. Evaluation Results
Our evaluation results suggest promising improvements in the model's creative writing capabilities. In the LLM Creative Story-Writing Benchmark evaluation, the model achieved a score of 8.33, showing an improvement from the base model's 7.87. When assessed on WritingBench, a comprehensive framework for evaluating large language model writing abilities, the model attained a score of 8.46. This places it in proximity to DeepSeek-R1's performance and represents an advancement over DeepSeek-R1-Distill-Qwen-14B's score of 7.93.
With respect to general capabilities, evaluations indicate modest improvements of 2%–5% in knowledge and reasoning tasks (CMMLU, MMLU-Pro), alongside encouraging progress in mathematical reasoning as measured by benchmarks such as AIME-2024, AIME-2025, and GSM8K. The results suggest that the model maintains a balanced performance profile, with improvements observed across creative writing, knowledge/reasoning, and mathematical tasks compared to DeepSeek-R1-Distill-Qwen-14B. These characteristics potentially make it suitable for a range of general-purpose applications.
4. How to Run Locally
Zhi-writing-dsr1-14b can be deployed on various hardware configurations, including GPUs with 80GB memory, a single H20/A800/H800, or dual RTX 4090. Additionally, the INT4 quantized version Zhi-writing-dsr1-14b-gptq-int4 can be deployed on a single RTX 4090.
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
MODEL_NAME = "Zhihu-ai/Zhi-writing-dsr1-14b"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
# use bf16
# model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto", trust_remote_code=True, bf16=True).eval()
# use fp16
# model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto", trust_remote_code=True, fp16=True).eval()
# use cpu only
# model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="cpu", trust_remote_code=True).eval()
# use auto mode, automatically select precision based on the device.
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
device_map="auto",
trust_remote_code=True
).eval()
# Specify hyperparameters for generation. But if you use transformers>=4.32.0, there is no need to do this.
# model.generation_config = GenerationConfig.from_pretrained(MODEL_NAME, trust_remote_code=True)
generate_configs = {
"temperature": 0.6,
"do_sample": True,
"top_p": 0.95,
"max_new_tokens": 4096
}
prompt = "请你以鲁迅的口吻，写一篇介绍西湖醋鱼的文章"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
**generate_configs
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
ZhiLight
You can easily start a service using ZhiLight
docker run -it --net=host --gpus='"device=0"' -v /path/to/model:/mnt/models --entrypoints="" ghcr.io/zhihu/zhilight/zhilight:0.4.17-cu124 python -m zhilight.server.openai.entrypoints.api_server --model-path /mnt/models --port 8000 --enable-reasoning --reasoning-parser deepseek-r1 --served-model-name Zhi-writing-dsr1-14b
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Zhi-writing-dsr1-14b",
"prompt": "请你以鲁迅的口吻，写一篇介绍西湖醋鱼的文章",
"max_tokens": 4096,
"temperature": 0.6,
"top_p": 0.95
}'
vllm
For instance, you can easily start a service using vLLM
# install vllm
pip install vllm>=0.6.4.post1
# huggingface model id
vllm serve Zhihu-ai/Zhi-writing-dsr1-14b --served-model-name Zhi-writing-dsr1-14b --port 8000
# local path
vllm serve /path/to/model --served-model-name Zhi-writing-dsr1-14b --port 8000
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Zhi-writing-dsr1-14b",
"prompt": "请你以鲁迅的口吻，写一篇介绍西湖醋鱼的文章",
"max_tokens": 4096,
"temperature": 0.6,
"top_p": 0.95
}'
SGLang
You can also easily start a service using SGLang
# install SGLang
pip install "sglang[all]>=0.4.5" --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer-python
# huggingface model id
python -m sglang.launch_server --model-path Zhihu-ai/Zhi-writing-dsr1-14b --served-model-name Zhi-writing-dsr1-14b --port 8000
# local path
python -m sglang.launch_server --model-path /path/to/model --served-model-name Zhi-writing-dsr1-14b --port 8000
# send request
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Zhi-writing-dsr1-14b",
"prompt": "请你以鲁迅的口吻，写一篇介绍西湖醋鱼的文章",
"max_tokens": 4096,
"temperature": 0.6,
"top_p": 0.95
}'
ollama
You can download ollama using this
- quantization: Q4_K_M
ollama run zhihu/zhi-writing-dsr1-14b
- bf16
ollama run zhihu/zhi-writing-dsr1-14b:bf16
5. Usage Recommendations
We recommend adhering to the following configurations when utilizing the Zhi-writing-dsr1-14b, including benchmarking, to achieve the expected performance:
Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs.
When evaluating model performance, it is recommended to conduct multiple tests and average the results. (We use
n=16andmax_tokens=32768for mathematical tasks andn=2for others)To ensure that the model engages in thorough reasoning like DeepSeek-R1 series models, we recommend enforcing the model to initiate its response with "<think>\n" at the beginning of every output.
6. Citation
@misc{Zhi-writing-dsr1-14b,
title={Zhi-writing-dsr1-14b: Curriculum Reinforcement and Direct Preference Optimization for Robust Creative Writing in LLMs},
author={Jiewu Wang, Xu Chen, Wenyuan Su, Chao Huang, Hongkui Gao, Lin Feng, Shan Wang, Lu Xu, Penghe Liu, Zebin Ou},
year={2025},
eprint={},
archivePrefix={},
url={https://huggingface.co/Zhihu-ai/Zhi-writing-dsr1-14b},
}
7. Contact
If you have any questions, please raise an issue or contact us at [email protected].
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deepseek-ai/DeepSeek-R1-Distill-Qwen-14B