Deepseek R1 0528 GGUF

🚀 DeepSeek-R1-0528

DeepSeek-R1-0528 is a minor version upgrade of the DeepSeek R1 model, which significantly enhances reasoning and inference capabilities, reduces hallucination rate, and offers better support for function calling and vibe coding.

🚀 Quick Start

• For DeepSeek-R1-0528-Qwen3-8B GGUFs, see here.
• Learn how to run DeepSeek-R1-0528 correctly - Read our Guide.
• See our collection for all versions of R1 including GGUF, 4-bit & 16-bit formats.
• Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

Usage Guidelines

• Set the temperature between 0.5–0.7 (0.6 recommended) to reduce repetition and incoherence.
• Set Top_P value of 0.95 (recommended)
• R1-0528 uses the same chat template as the original R1 model:

<｜begin▁of▁sentence｜><｜User｜>What is 1+1?<｜Assistant｜>It's 2.<｜end▁of▁sentence｜><｜User｜>Explain more!<｜Assistant｜>

• For llama.cpp / GGUF inference, you should skip the BOS since it’ll auto add it:

<｜User｜>What is 1+1?<｜Assistant｜>

• For complete detailed instructions, see our guide: unsloth.ai/blog/deepseek-r1-0528

✨ Features

The DeepSeek R1 model has undergone a minor version upgrade to DeepSeek-R1-0528. In the latest update, it significantly improves its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. It demonstrates outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic, with its overall performance approaching that of leading models, such as O3 and Gemini 2.5 Pro.

Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For example, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question.

Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and a better experience for vibe coding.

📚 Documentation

Evaluation Results

DeepSeek-R1-0528

For all models, the maximum generation length is set to 64K tokens. For benchmarks requiring sampling, a temperature of $0.6$, a top-p value of $0.95$, and 16 responses per query are used to estimate pass@1.

Note: The Agentless framework is used to evaluate model performance on SWE - Verified. Only text - only prompts are evaluated in HLE testsets. GPT - 4.1 is employed to act as the user role in Tau - bench evaluation.

DeepSeek-R1-0528-Qwen3-8B

The chain - of - thought from DeepSeek-R1-0528 is distilled to post - train Qwen3 8B Base, obtaining DeepSeek-R1-0528-Qwen3-8B. This model achieves state - of - the - art (SOTA) performance among open - source models on the AIME 2024, surpassing Qwen3 8B by +10.0% and matching the performance of Qwen3-235B - thinking.

Chat Website & API Platform

You can chat with DeepSeek-R1 on DeepSeek's official website: chat.deepseek.com, and switch on the button "DeepThink".

An OpenAI - Compatible API is also provided at DeepSeek Platform: platform.deepseek.com.

How to Run Locally

Please visit DeepSeek-R1 repository for more information about running DeepSeek-R1-0528 locally.

Compared to previous versions of DeepSeek-R1, the usage recommendations for DeepSeek-R1-0528 have the following changes:

1. System prompt is supported now.
2. It is not required to add "<think>\n" at the beginning of the output to force the model into thinking pattern.

The model architecture of DeepSeek-R1-0528-Qwen3-8B is identical to that of Qwen3-8B, but it shares the same tokenizer configuration as DeepSeek-R1-0528. This model can be run in the same manner as Qwen3-8B.

System Prompt

In the official DeepSeek web/app, the same system prompt with a specific date is used.

The assistant is DeepSeek-R1, created by DeepSeek Company.
Today is {current date}.

For example,

The assistant is DeepSeek-R1, created by DeepSeek Company.
Today is May 28, 2025, Monday.

Temperature

In the web and application environments, the temperature parameter $T_{model}$ is set to 0.6.

Prompts for File Uploading and Web Search

For file uploading, follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments.

file_template = \
"""[file name]: {file_name}
[file content begin]
{file_content}
[file content end]
{question}"""

For Web Search, {search_results}, {cur_date}, and {question} are arguments. For Chinese query, use the prompt:

search_answer_zh_template = \
'''# The following contents are the search results related to the user's message:
{search_results}
In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer.
When responding, please keep the following points in mind:
- Today is {cur_date}.
- Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question.
- For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary.
- For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough.
- If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content.
- For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content.
- Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability.
- Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage.
- Unless the user requests otherwise, your response should be in the same language as the user's question.
# The user's message is:
{question}'''

For English query, use the prompt:

search_answer_en_template = \
'''# The following contents are the search results related to the user's message:
{search_results}
In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer.
When responding, please keep the following points in mind:
- Today is {cur_date}.
- Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question.
- For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary.
- For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough.
- If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content.
- For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content.
- Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability.
- Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage.
- Unless the user requests otherwise, your response should be in the same language as the user's question.
# The user's message is:
{question}'''

📄 License

This code repository is licensed under MIT License. The use of DeepSeek-R1 models is also subject to MIT License. The DeepSeek-R1 series (including Base and Chat) supports commercial use and distillation.

🔧 Technical Details

@misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
      title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}, 
      author={DeepSeek-AI},
      year={2025},
      eprint={2501.12948},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.12948}, 
}

📞 Contact

If you have any questions, please raise an issue or contact us at service@deepseek.com.