Model card
Janus-Pro-1B is a compact, any-to-any multimodal model from the DeepSeek team designed to bridge the gap between text and visual processing in a lightweight footprint. Unlike traditional models that rely on separate encoders for different modalities, Janus-Pro utilizes a unified architecture to handle cross-modal understanding and generation. For developers, the 1B parameter scale is the primary draw, making it highly efficient for edge deployment, local prototyping, or integration into resource-constrained pipelines where latency is critical. While larger models offer higher reasoning depth, Janus-Pro provides a streamlined solution for tasks involving visual comprehension, image description, and multimodal interaction. It is particularly well-suited for developers building real-time vision-language applications or those looking to fine-tune a specialized multimodal agent without the massive overhead of trillion-parameter architectures. Its MIT license further simplifies commercial integration and open-source experimentation.
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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/Janus-Pro-1BInstall the CLI and SDK dependency before downloading.
pip install modelscopeDownload the complete weights, configuration and model card.
modelscope download --model deepseek-ai/Janus-Pro-1BREADME.md is used as an example; replace it with another repository file when needed.
modelscope download --model deepseek-ai/Janus-Pro-1B README.md --local_dir ./dirUseful in Python projects and automation scripts.
from modelscope import snapshot_download
model_dir = snapshot_download('deepseek-ai/Janus-Pro-1B')Make sure Git LFS is installed correctly.
git lfs install
git clone https://www.modelscope.cn/deepseek-ai/Janus-Pro-1B.gitFetch the repository structure first, then pull large files when needed.
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/deepseek-ai/Janus-Pro-1B.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.
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