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AtomicChat has published an edited version of DeepSeek-V4.1-Flash-1017 and says it removed refusals on the JailbreakBench test. Those figures have not been independently verified, and the edit reportedly removes refusals across topics rather than separating safety responses from political censorship. Compression tools let the model run on a PC with a 12 GB graphics card, but substantial system memory and storage are also required.
AtomicChat has published an edited version of DeepSeek-V4.1-Flash-1017 that it says refused none of the harmful prompts in its JailbreakBench test, compared with an 85.2% refusal rate for the unedited model. The claim has not been independently replicated. Compression work from IST Austria and the Strata engine is also presented as making the large model runnable on a PC with a single 12 GB graphics card, raising questions about local access to models without refusal safeguards and about how safety behavior relates to political refusals.
The repository, named DeepSeek-V4.1-Flash-1017-Uncensored-GSQ-RCO-GGUF, is hosted under the AtomicChat account on Hugging Face. AtomicChat describes its edit as removing refusals. Its model card reports a decline in refusals on JailbreakBench from 85.2% to 0.0% while reasoning scores held steady. Those are publisher-reported results; no independent reproduction or detailed external evaluation is provided in the source material.
The project combines three separate contributions: DeepSeek’s base model, compression methods developed by ISTA-DASLab at the Institute of Science and Technology Austria, and AtomicChat’s refusal-focused edit. The developers say the compressed version can run with 12 GB of GPU memory when paired with Strata, but it still needs 46 to 64 GB of system RAM, about 250 GB of downloads and, in their tests, roughly 5 to 10 tokens per second. The GPU figure alone does not describe the full hardware requirement.
AtomicChat says the edit removes refusals across topics rather than preserving a narrower set. The project’s card also acknowledges limits in its refusal measurement: its classifier checks the beginning of an answer, meaning a soft refusal later in the response may be counted as an answer. The source says the build is available without access restrictions and reported 30,284 downloads in the previous month and 221 likes on November 3, 2026. Those counts are a snapshot, not evidence of how many people ran the model or what they used it for.
One switch, two concerns DeepSeek on a gaming PC
AtomicChat says its edit removes refusals across topics. Compression may make the model runnable locally, but independent checks of its safety claims and hardware requirements are still missing.
Three contributions, one build
The project combines a base model, compression research and a separate edit targeting refusal behavior.
DeepSeek
V4.1-Flash-1017 supplies the underlying model.
ISTA-DASLab
Compression methods reduce model file sizes; they are separate from the safety edit.
AtomicChat
The publisher says its changes remove refusals across topics.
Local use needs more than a GPU
Developer-reported requirements and speed have not been independently benchmarked.
What the refusal result can tell you
The published score is a claim about the test, not proof of accuracy, neutrality or safer use.
- AtomicChat says refusals fell from 85.2% to 0.0% on JailbreakBench.
- The project says reasoning scores held steady.
- No independent replication or detailed external evaluation is provided.
- The classifier checks the answer’s beginning, so later soft refusals may be missed.
- Political response changes have not been measured for this build.
Local access shifts responsibility
Running a model on a desktop can change who sees and governs its use.
- Individuals may gain privacy or avoid hosted-service restrictions, while taking on more responsibility for judging outputs.
- Organizations may lose visibility through usual AI gateways, logging and policy controls.
- Legal duties depend on how a model is used; offline operation alone does not settle them.
Local Access Changes the Safety Picture
A model that can run locally may be used without sending prompts to a hosted service, which can appeal to people concerned about privacy or platform-level restrictions. The same arrangement can put activity outside a company’s usual AI gateways, logging and policy controls. The source frames that as a shadow-AI risk: staff could run the model on a desktop without an organization seeing or governing its use.
The central safety issue is not simply that one build is labeled uncensored. AtomicChat says the edit removes refusals across subjects, and the material does not establish that dangerous-request safeguards can be removed while political refusals remain—or vice versa. A cited US government evaluation found built-in Chinese Communist Party censorship in older DeepSeek versions, according to the source. Whether that political behavior changes in this edited model has not been measured.
For individual users, removing refusals does not make answers more accurate or politically neutral. It means the model may respond to requests the original declined, while the user takes on more responsibility for judging the output and its use. For organizations, the fact that software runs offline does not by itself settle legal or policy obligations. The source notes that EU AI Act duties depend on how a model is used, not simply on who removed its refusals; the specific obligations will depend on the deployment.
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Three Projects Behind the Build
DeepSeek released V4.1-Flash on September 10, 2026, then issued the V4.1-Flash-1017 update on October 17, according to the supplied account. DeepSeek described the update as “a Pareto improvement in performance and cost”; the announcement, as summarized in the source, did not discuss safety. The model card states that its repository and weights use the MIT License and contains no safety section or acceptable-use policy. The source reports that the model tree already included dozens of fine-tunes and many compressed versions.
ISTA-DASLab’s contribution is compression research, not a safety edit. Its methods—Gumbel-Softmax Quantization and a per-tensor bit budget—reduce the files needed to store the model. Strata is the free hobby engine described as making it possible to run the compressed build with a 12 GB GPU while holding much of the model in system memory. The source attributes the hardware and speed figures to developers’ own tests, rather than an independent benchmark.
AtomicChat’s separate contribution targets refusal behavior. The source characterizes the method as removing a direction associated with the model saying no, while the project card says large expert blocks were left as ISTA shipped them and that only a few shared components were changed. The publisher says the file sizes support that account, but outside reviewers have not checked the changes. The source also reports that Hugging Face’s “abliterated” tag filter listed 12,613 models on November 3, 2026, placing this release within an established community practice rather than presenting refusal removal as a new technique.
““The edit removes refusals across topics; it does not keep a narrower set of them.””
— AtomicChat, in the edited model’s card
Test Results and Bias Remain Unverified
The headline performance and safety claims remain self-reported. The source provides no independent replication of the 0.0% refusal result or confirmation that reasoning scores were maintained under a separate evaluation. AtomicChat’s stated classifier limitation also means the reported figure may not capture refusals that appear later in an answer.
It is also not clear whether removing refusal behavior changes the model’s political answers or its underlying biases. The source says older DeepSeek versions were found by US government evaluators to contain Chinese Communist Party censorship, but provides no equivalent measurement for this build. Nor has an outside party verified AtomicChat’s account of which model components were altered. Download numbers cannot establish how many users have successfully run the build or whether it is being used in business settings.
Independent Checks Are the Next Test
The next useful evidence would be independent testing of the refusal rate, reasoning performance and political response patterns, with methods that check entire answers rather than only their opening. Technical review could also verify what AtomicChat changed and whether the reported hardware, memory and speed requirements hold across different PCs.
Until those checks are available, the published results should be treated as the publisher’s claims, not settled performance findings. Organizations considering local model use will need to decide how to govern installations outside hosted AI services, while users should distinguish refusal removal from accuracy, neutrality or legal permission. The source identifies no scheduled independent evaluation or other specific next release.
Key Questions
What did AtomicChat publish?
It published an edited DeepSeek-V4.1-Flash-1017 model on Hugging Face, combining DeepSeek’s base model with ISTA-DASLab compression work and AtomicChat’s refusal-focused edit.
Has the model been independently shown to refuse 0% of harmful prompts?
No. The 0.0% figure is reported by the publisher and has not been independently replicated in the supplied material. AtomicChat also says its test classifier can miss soft refusals later in an answer.
Can it really run on a PC with a 12 GB graphics card?
The developers say it can run with 12 GB of GPU memory using compression and Strata, but also report a need for 46 to 64 GB of system RAM and about 250 GB of downloads. Their tests reportedly reached roughly 5 to 10 tokens per second.
Does “uncensored” mean the model is politically neutral?
No such finding is reported. AtomicChat says its edit removes refusals across topics, but the model’s political answers after the edit have not been measured. Removing refusals does not establish that a model is accurate or neutral.
What remains to be verified?
Independent reviewers have not confirmed the refusal and reasoning results, the exact model changes, or the reported performance across hardware. The effect of the edit on political bias and real-world use is also not established.
Source: Thorsten Meyer AI
