What is DeepSeek V4 Flash Abliterated?

A community build of DeepSeek V4 Flash (284B MoE, 13B active) with the refusal direction removed from the weights, published by huihui. Capabilities stay, canned refusals go. It ships as a single 154 GB GGUF at 4 bit under MIT and has passed 600K downloads, making it the most downloaded uncensored model of 2026 so far.

Does abliteration make the model dumber?

Slightly, sometimes. Removing the refusal direction can cost a small amount of benchmark performance, usually low single digit percentages. For most local use the difference is not noticeable, and the trade is a model that answers instead of lecturing.

Is there an abliterated version of the 0731 build yet?

Not yet as of release day; the popular abliteration is based on the preview checkpoint. Given that the preview abliteration appeared within weeks and became a 600K download hit, an 0731 abliteration is very likely on the way.

What hardware do I need for the abliterated V4 Flash?

Same as the base model: about 154 GB of weights at 4 bit, so a 192 GB Mac Studio or a workstation with 192 to 256 GB of RAM plus a GPU. Smaller machines should look at abliterated builds of the 9B distill class instead.

DeepSeek V4 Flash Abliterated: The Most Downloaded Uncensored Model of 2026

While the world talks about today's 0731 release, one V4 Flash number has been quietly climbing for weeks: the huihui abliterated build has passed 600K downloads, which makes it the most downloaded uncensored model of 2026 so far. Here is what it is, what abliteration changes, and how to run it.

What Abliteration Does

Instruction tuned models carry a learned refusal behavior that fires on a wide band of prompts, including plenty of harmless ones. Abliteration identifies the internal direction associated with refusing and removes it from the weights. No retraining, no new data: the model keeps its knowledge and skills but stops producing canned refusals. The long version, including the history and the trade offs, is in our abliterated models guide.

The trade offs are real but small: abliterated builds can drop a few benchmark points versus the base model, and tone can get a touch blunter. In exchange you get a model that treats you as the adult in the room, which is the entire point of running AI on your own hardware.

Why This One Took Off

Three reasons, stacking. First, the base model is genuinely frontier class at agent and code work while staying runnable (284B MoE, 13B active; see the hardware math). Second, MIT licensing means anyone can host, remix, and redistribute it without lawyers. Third, DeepSeek models have long been the community favorite for abliteration because the results hold up: capabilities survive the surgery unusually well. Put frontier ability, permissive license, and clean abliteration together, and 600K downloads stops being surprising.

The Practical Details

	V4 Flash Abliterated
Base	DeepSeek V4 Flash Preview (284B MoE, 13B active)
File	Single GGUF, Q4_K, 154 GB
License	MIT
Hardware	Same as base: 192 GB memory class (Mac Studio 192 GB, or 192 to 256 GB DDR5 + GPU)
0731 version	Not yet; expect the community to abliterate the new checkpoint soon

How to Run It

Locally Uncensored ships it in the built in catalog. Install the app, open the Models tab, pick DeepSeek V4 Flash Abliterated, click download. The app checks your memory before the 154 GB starts moving and serves the model through any of its 12 supported local backends. Full setup context in the local guide.

If your machine is smaller than the 192 GB class, you do not have to leave the uncensored lane: the catalog carries abliterated builds from 8B to 70B (DeepSeek R1 distills, Qwen, Gemma Heretic and friends), ranked by VRAM in our best uncensored models roundup.

A Note on Responsibility

An uncensored model is a tool without a chaperone, not an endorsement of everything it can say. You remain responsible for what you create with it, same as with a text editor. Local means private, and private means the judgment calls are yours.