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MODEL Listed

DeepSeek-V4.1-Flash-UNCENSORED-FP8

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.

dealignaiimage text to text
01 / MODEL CARD

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 typeimage text to text
Providerdealignai
Licensemit
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8
View model source
Version informationUse the source repository for the latest version
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03 / DOWNLOAD

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.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8
Download one file to a local directory

README.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 ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8.git
Clone without downloading LFS blobs

Fetch 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.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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