Model card
DeepSeek-V4.1-Flash-UNCENSORED-FP8 is a high-throughput multimodal model optimized for low-latency vision-language tasks. Built on the Flash architecture and quantized to FP8, it strikes a balance between rapid inference speeds and significant memory savings, making it ideal for edge deployment or cost-sensitive scaling. Unlike standard vision models that struggle with restrictive alignment, this iteration is tuned for high instruction-following fidelity across diverse visual contexts without heavy-handed filtering. For developers, this means more reliable performance in complex OCR, visual reasoning, and document analysis workflows where precision is non-negotiable. It integrates seamlessly into standard Hugging Face pipelines, offering a streamlined path for those needing to process image-text pairs in real-time applications such as automated visual inspection or interactive multimodal agents.
Model files and versions
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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.
dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8Install the CLI and SDK dependency before downloading.
pip install modelscopeDownload the complete weights, configuration and model card.
modelscope download --model dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8README.md is used as an example; replace it with another repository file when needed.
modelscope download --model dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8 README.md --local_dir ./dirUseful in Python projects and automation scripts.
from modelscope import snapshot_download
model_dir = snapshot_download('dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8')Make sure Git LFS is installed correctly.
git lfs install
git clone https://www.modelscope.cn/dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8.gitFetch the repository structure first, then pull large files when needed.
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8.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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