QuantTrio/DeepSeek-V3.1-AWQ

DeepSeek-V3.1-AWQ

Base model: DeepSeek-V3.1

【Dependencies / Installation】

As of 2025-08-27, create a fresh Python environment and run:

# ❗there are glitches with vllm 0.10.1.1, still looking for resolutions❗
# ❗downgrade vllm for now ❗
pip install vllm==0.9.2 transformers==4.53.0

SITE_PACKAGES=$(pip -V | awk '{print $4}' | sed 's/\/pip$//')
# ❗patch up AWQ MoE quant config, otherwise some modules cannot be properly loaded❗
cp awq_marlin.py "$SITE_PACKAGES/vllm/model_executor/layers/quantization/awq_marlin.py"
# ❗patch up for fp32 e_score_correction_bias, see https://www.github.com/vllm-project/vllm/pull/23640❗
cp deepseek_v2.py "$SITE_PACKAGES/vllm/model_executor/models/deepseek_v2.py"

【vLLM Single Node with 8 GPUs — Startup Command】

CONTEXT_LENGTH=32768

vllm serve \
    QuantTrio/DeepSeek-V3.1-AWQ \
    --served-model-name DeepSeek-V3.1-AWQ \
    --swap-space 16 \
    --max-num-seqs 512 \
    --max-model-len $CONTEXT_LENGTH \
    --max-seq-len-to-capture $CONTEXT_LENGTH \
    --gpu-memory-utilization 0.8 \
    --tensor-parallel-size 8 \
    --trust-remote-code \
    --disable-log-requests \
    --host 0.0.0.0 \
    --port 8000

【Logs】

2025-08-27
1. new installation instuction for stable/correct performance
  (a) use vllm 0.9.2 instead of 0.9.0:
      there is unidentified issue with 0.9.0 🥹 which causes numerical error
  (b) patch up deepseek_v2.py for fp32 e_score_correction_bias

2025-08-23
1. Initial commit

【Model Files】

【Model Download】

from huggingface_hub import snapshot_download
snapshot_download('QuantTrio/DeepSeek-V3.1-AWQ', cache_dir="your_local_path")

【Overview】

<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/deepseekai" 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

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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

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

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 repo for more information about running this model locally.

License

This repository and the model weights are licensed under the MIT 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.