| | --- |
| | license: mit |
| | library_name: transformers |
| | --- |
| | # DeepSeek-V3.1 |
| |
|
| | <!-- markdownlint-disable first-line-h1 --> |
| | <!-- markdownlint-disable html --> |
| | <!-- markdownlint-disable no-duplicate-header --> |
| |
|
| | <div align="center"> |
| | <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" /> |
| | </div> |
| | <hr> |
| | <div align="center" style="line-height: 1;"> |
| | <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;"> |
| | <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/> |
| | </a> |
| | <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;"> |
| | <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V3-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> |
| | </a> |
| | <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;"> |
| | <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> |
| | </a> |
| | </div> |
| | |
| | <div align="center" style="line-height: 1;"> |
| | <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;"> |
| | <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> |
| | </a> |
| | <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;"> |
| | <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/> |
| | </a> |
| | <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;"> |
| | <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> |
| | </a> |
| | </div> |
| | |
| | <div align="center" style="line-height: 1;"> |
| | <a href="LICENSE" style="margin: 2px;"> |
| | <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> |
| | </a> |
| | </div> |
| | |
| | ## Introduction |
| |
|
| | DeepSeek-V3.1 is a hybrid model that supports both thinking mode and non-thinking mode. Compared to the previous version, this upgrade brings improvements in multiple aspects: |
| |
|
| | - **Hybrid thinking mode**: One model supports both thinking mode and non-thinking mode by changing the chat template. |
| |
|
| | - **Smarter tool calling**: Through post-training optimization, the model's performance in tool usage and agent tasks has significantly improved. |
| |
|
| | - **Higher thinking efficiency**: DeepSeek-V3.1-Think achieves comparable answer quality to DeepSeek-R1-0528, while responding more quickly. |
| |
|
| | DeepSeek-V3.1 is post-trained on the top of DeepSeek-V3.1-Base, which is built upon the original V3 base checkpoint through a two-phase long context extension approach, following the methodology outlined in the original DeepSeek-V3 report. We have expanded our dataset by collecting additional long documents and substantially extending both training phases. The 32K extension phase has been increased 10-fold to 630B tokens, while the 128K extension phase has been extended by 3.3x to 209B tokens. Additionally, DeepSeek-V3.1 is trained using the UE8M0 FP8 scale data format to ensure compatibility with microscaling data formats. |
| |
|
| | ## Model Downloads |
| |
|
| | <div align="center"> |
| |
|
| | | **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** | |
| | | :------------: | :------------: | :------------: | :------------: | :------------: | |
| | | DeepSeek-V3.1-Base | 671B | 37B | 128K | [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Base) \| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V3.1-Base) | |
| | | DeepSeek-V3.1 | 671B | 37B | 128K | [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3.1) \| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V3.1) | |
| |
|
| | </div> |
| |
|
| | ## Chat Template |
| |
|
| | The details of our chat template is described in `tokenizer_config.json` and `assets/chat_template.jinja`. Here is a brief description. |
| |
|
| | ### Non-Thinking |
| |
|
| | #### First-Turn |
| |
|
| | Prefix: |
| | `<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜></think>` |
| |
|
| | With the given prefix, DeepSeek V3.1 generates responses to queries in non-thinking mode. Unlike DeepSeek V3, it introduces an additional token `</think>`. |
| |
|
| | #### Multi-Turn |
| | Context: |
| | `<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>...<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>` |
| |
|
| | Prefix: |
| | `<｜User｜>{query}<｜Assistant｜></think>` |
| |
|
| | By concatenating the context and the prefix, we obtain the correct prompt for the query. |
| |
|
| | ### Thinking |
| |
|
| | #### First-Turn |
| | Prefix: |
| | `<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜><think>` |
| |
|
| | The prefix of thinking mode is similar to DeepSeek-R1. |
| |
|
| |
|
| | #### Multi-Turn |
| | Context: |
| | `<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>...<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>` |
| |
|
| | Prefix: |
| | `<｜User｜>{query}<｜Assistant｜><think>` |
| |
|
| | The multi-turn template is the same with non-thinking multi-turn chat template. It means the thinking token in the last turn will be dropped but the `</think>` is retained in every turn of context. |
| |
|
| | ### ToolCall |
| | Toolcall is supported in non-thinking mode. The format is: |
| |
|
| | `<｜begin▁of▁sentence｜>{system prompt}{tool_description}<｜User｜>{query}<｜Assistant｜></think>` where the tool_description is |
| | |
| | ``` |
| | ## Tools |
| | You have access to the following tools: |
| | |
| | ### {tool_name1} |
| | Description: {description} |
| |
|
| | Parameters: {json.dumps(parameters)} |
| |
|
| | IMPORTANT: ALWAYS adhere to this exact format for tool use: |
| | <｜tool▁calls▁begin｜><｜tool▁call▁begin｜>tool_call_name<｜tool▁sep｜>tool_call_arguments<｜tool▁call▁end｜>{{additional_tool_calls}}<｜tool▁calls▁end｜> |
| |
|
| | Where: |
| | - `tool_call_name` must be an exact match to one of the available tools |
| | - `tool_call_arguments` must be valid JSON that strictly follows the tool's Parameters Schema |
| | - For multiple tool calls, chain them directly without separators or spaces |
| | ``` |
| | |
| | ### Code-Agent |
| | We support various code agent frameworks. Please refer to the above toolcall format to create your own code agents. An example is shown in `assets/code_agent_trajectory.html`. |
| | |
| | ### Search-Agent |
| | We design a specific format for searching toolcall in thinking mode, to support search agent. |
| | |
| | For complex questions that require accessing external or up-to-date information, DeepSeek-V3.1 can leverage a user-provided search tool through a multi-turn tool-calling process. |
| | |
| | Please refer to the `assets/search_tool_trajectory.html` and `assets/search_python_tool_trajectory.html` for the detailed template. |
| | |
| | ## Evaluation |
| | | Category | Benchmark (Metric) | DeepSeek V3.1-NonThinking | DeepSeek V3 0324 | DeepSeek V3.1-Thinking | DeepSeek R1 0528 |
| | |----------|----------------------------------|-----------------|---|---|---| |
| | | General | |
| | | | MMLU-Redux (EM) | 91.8 | 90.5 | 93.7 | 93.4 |
| | | | MMLU-Pro (EM) | 83.7 | 81.2 | 84.8 | 85.0 |
| | | | GPQA-Diamond (Pass@1) | 74.9 | 68.4 | 80.1 | 81.0 |
| | | | Humanity's Last Exam (Pass@1) | - | - | 15.9 | 17.7 |
| | |Search Agent| |
| | | | BrowseComp | - | - | 30.0 | 8.9 |
| | | | BrowseComp_zh | - | - | 49.2 | 35.7 |
| | | | Humanity's Last Exam (Python + Search) |- | - | 29.8 | 24.8 |
| | | | SimpleQA | - | - | 93.4 | 92.3 |
| | | Code | |
| | | | LiveCodeBench (2408-2505) (Pass@1) | 56.4 | 43.0 | 74.8 | 73.3 |
| | | | Codeforces-Div1 (Rating) | - | - | 2091 | 1930 |
| | | | Aider-Polyglot (Acc.) | 68.4 | 55.1 | 76.3 | 71.6 |
| | | Code Agent| |
| | | | SWE Verified (Agent mode) | 66.0 | 45.4 | - | 44.6 |
| | | | SWE-bench Multilingual (Agent mode) | 54.5 | 29.3 | - | 30.5 |
| | | | Terminal-bench (Terminus 1 framework) | 31.3 | 13.3 | - | 5.7 |
| | | Math | |
| | | | AIME 2024 (Pass@1) | 66.3 | 59.4 | 93.1 | 91.4 |
| | | | AIME 2025 (Pass@1) | 49.8 | 51.3 | 88.4 | 87.5 |
| | | | HMMT 2025 (Pass@1) | 33.5 | 29.2 | 84.2 | 79.4 | |
| | |
| | Note: |
| | - Search agents are evaluated with our internal search framework, which uses a commercial search API + webpage filter + 128K context window. Seach agent results of R1-0528 are evaluated with a pre-defined workflow. |
| | |
| | - SWE-bench is evaluated with our internal code agent framework. |
| | |
| | - HLE is evaluated with the text-only subset. |
| | |
| | ### Usage Example |
| | |
| | ```python |
| | import transformers |
| |
|
| | tokenizer = transformers.AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3.1") |
| | |
| | messages = [ |
| | {"role": "system", "content": "You are a helpful assistant"}, |
| | {"role": "user", "content": "Who are you?"}, |
| | {"role": "assistant", "content": "<think>Hmm</think>I am DeepSeek"}, |
| | {"role": "user", "content": "1+1=?"} |
| | ] |
| | |
| | tokenizer.apply_chat_template(messages, tokenize=False, thinking=True, add_generation_prompt=True) |
| | # '<｜begin▁of▁sentence｜>You are a helpful assistant<｜User｜>Who are you?<｜Assistant｜></think>I am DeepSeek<｜end▁of▁sentence｜><｜User｜>1+1=?<｜Assistant｜><think>' |
| | |
| | tokenizer.apply_chat_template(messages, tokenize=False, thinking=False, add_generation_prompt=True) |
| | # '<｜begin▁of▁sentence｜>You are a helpful assistant<｜User｜>Who are you?<｜Assistant｜></think>I am DeepSeek<｜end▁of▁sentence｜><｜User｜>1+1=?<｜Assistant｜></think>' |
| | ``` |
| | |
| | ## How to Run Locally |
| | |
| | The model structure of DeepSeek-V3.1 is the same as DeepSeek-V3. Please visit [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repo for more information about running this model locally. |
| | |
| | ## License |
| | |
| | This repository and the model weights are licensed under the [MIT License](LICENSE). |
| | |
| | ## Citation |
| | |
| | ``` |
| | @misc{deepseekai2024deepseekv3technicalreport, |
| | title={DeepSeek-V3 Technical Report}, |
| | author={DeepSeek-AI}, |
| | year={2024}, |
| | eprint={2412.19437}, |
| | archivePrefix={arXiv}, |
| | primaryClass={cs.CL}, |
| | url={https://arxiv.org/abs/2412.19437}, |
| | } |
| | ``` |
| | |
| | ## Contact |
| | |
| | If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com). |
| | |
