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Model: zhihu/Zhi-Create-DSR1-14B-GPTQ-INT4 Source: Original Platform
2026-06-06 07:44:12 +08:00
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+---
+license: apache-2.0
+datasets:
+- Congliu/Chinese-DeepSeek-R1-Distill-data-110k
+- cognitivecomputations/dolphin-r1
+- open-thoughts/OpenThoughts-114k
+- qihoo360/Light-R1-SFTData
+- qihoo360/Light-R1-DPOData
+language:
+- zh
+- en
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
+tags:
+- qwen2
+library_name: transformers
+---
+# Zhi-Create-DSR1-14B
+
+## 1. Introduction
+
+Zhi-Create-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](https://github.com/lechmazur/writing), the model achieved a score of **8.33** compared to its base model's **7.8**. In the [WritingBench](https://github.com/X-PLUG/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:
+
+![writingbench](./images/writingbench_score.png)
+
+<figcaption style="text-align:center; font-size:0.9em; color:#666">
+Figure 1: WritingBench performance of Zhi-Create-DSR1-14B and DeepSeek-R1-Distill-Qwen-14B across 6 domains and 3 writing requirements evaluated with WritingBench critic model (scale: 1-10). The six domains include: (D1) Academic & Engineering, (D2) Finance & Business, (D3) Politics & Law, (D4) Literature & Art, (D5) Education, and (D6) Advertising & Marketing. The three writing requirements assessed are: (R1) Style, (R2) Format, and (R3) Length. Here, "C" indicates category-specific scores.
+</figcaption>
+
+## 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](https://huggingface.co/datasets/cognitivecomputations/dolphin-r1), [Congliu/Chinese-DeepSeek-R1-Distill-data-110k](https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k), [OpenThoughts-114k](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k), [Light-R1-SFTData](https://huggingface.co/datasets/qihoo360/Light-R1-SFTData), and [Light-R1-DPOData](https://huggingface.co/datasets/qihoo360/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](https://arxiv.org/abs/2406.18629)) to selectively penalize incorrect tokens, while incorporating positive constraints in the loss function as proposed in DPOP ([arXiv:2402.13228](https://arxiv.org/abs/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. We conducted additional evaluations on the instruction-following ifeval benchmark, with experimental results demonstrating a performance improvement in model capabilities from an initial score of **71.43** to an enhanced score of **74.71**.
+
+![general](./images/general_score.png)
+
+<figcaption style="text-align:center; font-size:0.9em; color:#666">
+Figure 2: When evaluating model performance, it is recommended to conduct multiple tests and average the results. (We use n=16 and max_tokens=32768 for mathematical tasks and n=2 for others)
+</figcaption>
+
+## 4. How to Run Locally
+
+Zhi-Create-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-Create-DSR1-14B-GPTQ-INT4 can be deployed on a single RTX 4090.
+
+### Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation import GenerationConfig
+
+MODEL_NAME = "Zhihu-ai/Zhi-Create-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](https://github.com/zhihu/ZhiLight)
+
+```bash
+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-Create-DSR1-14B
+
+curl http://localhost:8000/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "Zhi-Create-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](https://github.com/vllm-project/vllm)
+
+```bash
+# install vllm
+pip install vllm>=0.6.4.post1
+
+# huggingface model id
+vllm serve Zhihu-ai/Zhi-Create-DSR1-14B --served-model-name Zhi-Create-DSR1-14B --port 8000
+
+# local path
+vllm serve /path/to/model  --served-model-name Zhi-Create-DSR1-14B --port 8000
+
+curl http://localhost:8000/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "Zhi-Create-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](https://github.com/sgl-project/sglang)
+
+```bash
+# 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-Create-DSR1-14B --served-model-name Zhi-Create-DSR1-14B --port 8000
+
+# local path
+python -m sglang.launch_server --model-path /path/to/model  --served-model-name Zhi-Create-DSR1-14B --port 8000
+
+# send request
+curl http://localhost:8000/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "Zhi-Create-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](https://ollama.com/download/)
+
+* quantization: Q4_K_M
+
+```bash
+ollama run zhihu/zhi-create-dsr1-14b
+```
+
+* bf16
+
+```bash
+ollama run zhihu/zhi-create-dsr1-14b:bf16
+```
+
+## 5. Usage Recommendations
+
+We recommend adhering to the following configurations when utilizing the Zhi-Create-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=16` and `max_tokens=32768` for mathematical tasks and `n=2` for 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
+
+```text
+@misc{Zhi-Create-DSR1-14B,
+      title={Zhi-Create-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-Create-DSR1-14B}, 
+}
+```
+
+## 7. Contact
+
+If you have any questions, please raise an issue or contact us at [ai@zhihu.com](mailto:ai@zhihu.com).