Model card
DeepSeek-V4-Pro is a high-performance text generation model designed for developers requiring advanced reasoning and complex instruction following. Unlike general-purpose chat models, this iteration focuses on optimizing throughput and logical consistency, making it a strong candidate for agentic workflows, automated code generation, and sophisticated RAG pipelines. For engineers integrating LLMs into production environments, the model offers a competitive alternative to closed-source APIs by providing high-density intelligence with a focus on mathematical and programming proficiency. It is built to handle nuanced multi-turn dialogues and structured data extraction with minimal hallucination. Because it is released under the MIT license, it provides significant flexibility for commercial deployment and fine-tuning within private infrastructure, allowing for deep integration into existing DevOps and software development lifecycles without the heavy constraints of proprietary ecosystems.
Model files and versions
Download this model
We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.
deepseek-ai/DeepSeek-V4-ProInstall the CLI and SDK dependency before downloading.
pip install modelscopeDownload the complete weights, configuration and model card.
modelscope download --model deepseek-ai/DeepSeek-V4-ProREADME.md is used as an example; replace it with another repository file when needed.
modelscope download --model deepseek-ai/DeepSeek-V4-Pro README.md --local_dir ./dirUseful in Python projects and automation scripts.
from modelscope import snapshot_download
model_dir = snapshot_download('deepseek-ai/DeepSeek-V4-Pro')Make sure Git LFS is installed correctly.
git lfs install
git clone https://www.modelscope.cn/deepseek-ai/DeepSeek-V4-Pro.gitFetch the repository structure first, then pull large files when needed.
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/deepseek-ai/DeepSeek-V4-Pro.gitHow to use
- 01Step 1
Read the model card and source information.
- 02Step 2
Start with a small, non-sensitive evaluation.
- 03Step 3
Review quality, licensing and usage limits.
- 04Step 4
Adopt it only after validation.
Discussions
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