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AtomicChat has published an edited, compressed version of DeepSeek-V4.1-Flash-1017, saying it runs on a PC with a 12 GB graphics card and recorded a 0% refusal rate on its JailbreakBench test. The performance and safety figures are self-reported and have not been independently replicated; the edit removes refusals across topics, and its effect on the model’s political bias has not been measured.
The Hugging Face repository is named DeepSeek-V4.1-Flash-1017-Uncensored-GSQ-RCO-GGUF and is published under the AtomicChat account. It combines DeepSeek’s base model, compression methods developed by ISTA-DASLab at the Institute of Science and Technology Austria, and AtomicChat’s modification to remove refusals. The repository had 30,284 downloads in the previous month and 221 likes on November 3, according to the source material.
AtomicChat reports that the model’s refusal rate on JailbreakBench fell from 85.2% to 0.0%, while reasoning scores held. Those results are self-reported. The publisher also says the model can run with one 12 GB GPU, but its own stated requirements include 46 to 64 GB of system RAM, about 250 GB of downloads and performance of roughly 5 to 10 tokens per second.
The edit targets a direction in the model associated with refusal behavior. AtomicChat says the model’s large expert blocks were left as supplied by ISTA-DASLab and that some shared components were changed. The source says file sizes are consistent with that account, but no outside party has verified the changes. DeepSeek’s base model is MIT-licensed, and its model card, as described in the source, contains no safety section or acceptable-use policy.
Local AI • Model edit • Publisher-reported claims
DeepSeek Uncensored on a Gaming PC: One Switch Controls Both Safety and Censorship
AtomicChat says its compressed edit of DeepSeek-V4.1-Flash-1017 runs on a single 12 GB GPU and recorded a 0% refusal rate in its JailbreakBench test. The results have not been independently replicated, and the edit removes refusals across topics.
Fewer built-in refusals can mean fewer barriers across every topic.
01 / What was published
A community build joins three separate projects
The Hugging Face repository, published under the AtomicChat account, combines DeepSeek’s base model, compression work from ISTA-DASLab, and AtomicChat’s modification to remove refusal behavior.
DeepSeek base
V4.1-Flash was released September 10, 2026; the 1017 update followed October 17.
ISTA-DASLab compression
Gumbel-Softmax Quantization and a per-tensor bit budget reduce model size.
Strata execution
The free engine uses both GPU memory and system memory to run the compressed model.
AtomicChat edit
A modification targets a model direction associated with refusal behavior.
02 / Reported test result
Refusals fell in AtomicChat’s test
AtomicChat reports that the refusal rate on JailbreakBench moved from 85.2% to 0.0%, while reasoning scores held. These are publisher-reported results; no independent replication is described.
The graphic shows the publisher’s stated figures, not an independently verified benchmark. AtomicChat says its classifier reads the start of an answer, so a softer refusal later in the text could pass as a response.
Refusals removed broadly
The edit removes refusals across topics. It does not preserve a narrower set of refusal boundaries.
Reasoning scores
The publisher says reasoning held, but the source material provides no outside replication.
Political responses
Whether this edit changes political bias, or leaves it intact, has not been measured in the cited material.
03 / Local use and governance
Running locally changes who can see and manage use
A local model may appeal to people who want prompts to stay on their own devices or who object to platform restrictions. The same setup can put usage beyond an organization’s usual controls.
More control, fewer built-in barriers
Local execution can keep prompts on a personal device. Removing refusal behavior also means users encounter fewer internal barriers to requests the original model might decline. AtomicChat says users are responsible for their use and for following applicable laws.
Local installations can escape managed systems
Employees could run a capable model outside company gateways, logs, and policies. Offline installations may be harder to identify and govern. The source does not establish how often this would lead to real-world incidents.
04 / Hardware reality check
A 12 GB GPU is only part of the requirement
AtomicChat says Strata can run the model with one 12 GB GPU by using system memory for the rest. The publisher’s own stated requirements include substantial RAM and storage.
12 GB GPU
Publisher-stated minimum for one graphics card. Performance across different PCs and configurations has not been independently checked.
46–64 GB RAM
Required alongside GPU memory, according to the developer’s own description.
About 250 GB
Downloads are about 250 GB; the publisher estimates roughly 5–10 tokens per second.
Actual speed may vary with hardware, prompts, and configuration. No independent performance test is cited.
05 / What remains unchecked
Independent testing is the next check
The cited material does not identify a scheduled evaluation, a response from DeepSeek, or a planned update from AtomicChat.
Can the refusal and reasoning results be reproduced?
Independent replication would clarify how the model performs beyond the publisher’s stated test and classifier.
What happens across political prompts?
The source cites prior evaluations of built-in censorship in older DeepSeek versions, but gives no details here and does not measure this edited build.
How consistent is local performance?
Hardware claims need checks across PCs, prompts, and configurations.
06 / Key questions
What readers should know
What did AtomicChat publish?
An edited and compressed build of DeepSeek-V4.1-Flash-1017 on Hugging Face, named DeepSeek-V4.1-Flash-1017-Uncensored-GSQ-RCO-GGUF.
Does it really refuse 0% of harmful requests?
That is AtomicChat’s self-reported JailbreakBench result. It has not been independently replicated in the source material, and the publisher describes a limitation in how its classifier checks answers.
Does removing refusals remove political bias?
That has not been measured. Removing refusal behavior across topics does not establish whether political bias in the model’s answers changes.
What should organizations watch for?
Employees may run local models outside company gateways, logs, and policies. Regulatory responsibilities depend on use and cannot be determined from repository details alone.
A Desktop Model Outside Managed Systems
A model that can be run locally may appeal to users who want their prompts to stay on their own devices or who object to platform restrictions. Removing refusals also means users encounter fewer built-in barriers when making requests the original model would decline. The publisher warns that people using the build are responsible for their use and for complying with applicable law.
For organizations, the source identifies a practical governance concern: employees could run a capable model locally, outside company gateways, logging and policy controls. An offline installation may be harder for an organization to monitor or manage. Removing refusal behavior also takes away one internal response to harmful prompts, although the material does not establish how often that would lead to real-world incidents.
Regulatory responsibilities are not settled by who made the edit. The source says duties under the EU AI Act depend on how a model is used. The specific obligations for a given organization or deployment cannot be determined from the repository details alone.
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Three Projects Behind the Build
DeepSeek released V4.1-Flash on September 10, 2026, then published the V4.1-Flash-1017 update on October 17, according to the source. DeepSeek described that update as “a Pareto improvement in performance and cost.” The cited announcement did not discuss safety. The base model’s repository and weights carry an MIT license, as stated on its model card.
The ability to run a model of this size on consumer hardware comes from separate compression work. ISTA-DASLab developed Gumbel-Softmax Quantization and a per-tensor bit budget, while the free Strata engine runs the compressed model using both GPU memory and system memory. That work concerns model size and execution; the source says it is separate from AtomicChat’s removal of refusals.
Removing refusal behavior from chat models is not a new technique. The source says a 2024 research paper described refusal as depending on a single direction in a model, and that Hugging Face’s “abliterated” tag filter listed 12,613 models on November 3, 2026. The development highlighted here is the combination of that edit with a large model compressed for a single consumer GPU.
““The edit removes refusals across topics; it does not keep a narrower set of them.””
— AtomicChat repository card
Test Results And Bias Remain Unchecked
No independent replication of AtomicChat’s refusal or reasoning results is described in the source. The publisher also notes that its refusal classifier examines the beginning of an answer, so a later soft refusal could be counted as a successful response. The 0.0% figure should therefore be read as the publisher’s result under its stated test, not as proof that the model will never refuse.
The source says U.S. government evaluators found built-in Chinese Communist Party censorship in older DeepSeek versions. It does not provide details of those evaluations here. Whether removing refusal behavior from this build also changes political bias, or leaves it intact, has not been measured in the cited material.
It is also unclear how well the stated hardware and speed figures hold across different PCs, prompts and configurations. AtomicChat’s own figures require substantial system memory and storage, and no independent performance testing is cited.
Independent Testing Is The Next Check
The immediate next step for assessing the claims would be independent replication of the refusal and reasoning tests, alongside checks of the model’s political responses and behavior across different prompts. The source material does not identify a scheduled evaluation, response from DeepSeek, or planned update from AtomicChat.
For organizations considering local model use, the practical question is whether they can identify and govern installations running outside managed AI services. For individual users, the published requirements offer a starting point for checking whether their PC has enough memory and storage, but actual performance may vary. Until outside testing is available, the headline safety and hardware claims remain publisher-reported.
Key Questions
What did AtomicChat publish?
It published an edited and compressed build of DeepSeek-V4.1-Flash-1017 on Hugging Face, under the name DeepSeek-V4.1-Flash-1017-Uncensored-GSQ-RCO-GGUF.
Does it really refuse 0% of harmful requests?
That is AtomicChat’s self-reported result on JailbreakBench. It has not been independently replicated in the source material, and the publisher describes a limitation in how its refusal classifier checks answers.
Can it run on a PC with a 12 GB graphics card?
The developers say it can run on one 12 GB GPU with Strata, using system memory for the rest of the model. Their stated requirements are 46 to 64 GB of RAM, about 250 GB of downloads and roughly 5 to 10 tokens per second; independent checks are not cited.
Does removing refusals remove political bias?
That has not been measured in the cited material. AtomicChat says the edit removes refusals across topics, but that does not establish whether political bias in the model’s answers changes.
What should organizations watch for?
The source points to the possibility of employees running local models outside company gateways, logs and policies. Whether a particular deployment creates regulatory obligations depends on its use and circumstances.
Source: Thorsten Meyer AI
