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AtomicChat has published an edited DeepSeek V4.1-Flash-1017 model that it says runs on one 12GB GPU and scored 0% refusals on its JailbreakBench test. The results are self-reported, and the build requires substantial system RAM and storage; whether the edit changes the model’s political biases has not been measured.
The Hugging Face repository, named DeepSeek-V4.1-Flash-1017-Uncensored-GSQ-RCO-GGUF, combines DeepSeek’s model with compression work from ISTA-DASLab at the Institute of Science and Technology Austria and an edit by AtomicChat. The compression methods reduce the files’ size; AtomicChat says its edit removes a refusal-related direction in the model. These are distinct steps, and the compression research itself does not remove safety refusals.
AtomicChat says its version recorded 0.0% refusals on JailbreakBench, compared with 85.2% for the original, while reasoning scores held. The publisher also warns that its refusal classifier checks the beginning of an answer, so a soft refusal later in the text may be counted as an answer. It says the edit removes refusals across topics rather than preserving a narrower set. Neither the benchmark results nor the claim that only limited shared model components were changed has been independently checked.
The single-GPU claim comes with significant resource requirements. According to the developers’ own tests, the build needs 46–64GB of system RAM, about 250GB of downloads and runs at roughly 5–10 tokens per second. The repository was publicly accessible and showed 30,284 downloads in the previous month and 221 likes on November 3, according to the source material.
A Local Model With Fewer Refusals
A capable model that can run locally may appeal to people seeking offline access and greater privacy. The same setup can also sit outside an organization’s usual AI gateways, logging and usage rules. For companies, that creates a potential shadow AI issue: employees could use a substantial model on a desktop without the monitoring their organization applies to hosted services.
Removing refusals also removes one layer of restraint against harmful requests and may leave the model with fewer defenses against prompt injection. The source says duties under the EU AI Act depend on how a model is used, rather than on who removed its refusals. For individual users, fewer platform restrictions do not make a model neutral, accurate or safe. The publisher says users are responsible for their use and compliance with applicable law.
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How the Build Was Assembled
DeepSeek released V4.1-Flash on September 10, 2026, followed by the V4.1-Flash-1017 update on October 17. DeepSeek described that update as “a Pareto improvement in performance and cost”; its announcement did not address safety. The model card says the weights are licensed under the MIT License and, according to the source material, does not include an acceptable-use policy.
ISTA-DASLab’s contribution is compression research using Gumbel-Softmax Quantization and a per-tensor bit budget. Strata, a free hobby engine, is used to run the compressed model with some of it held in system memory. AtomicChat then applied its refusal-removing edit. Removing refusals from chat models is an established technique: the source cites a 2024 research paper on a refusal-related direction and says Hugging Face’s “abliterated” tag listed 12,613 models on November 3, 2026.
““a Pareto improvement in performance and cost””
— DeepSeek’s announcement of V4.1-Flash-1017
Performance and Bias Still Unverified
Independent replication is lacking for the refusal rate, reasoning scores, hardware performance and claim that the larger expert blocks were left unchanged. The publisher says some model variants did not clear every evaluation gate and were published as measured; it also acknowledges that its refusal classifier can miss soft refusals later in an answer.
It is also unknown whether removing refusals changes any political slant in the model’s responses. The source says US government evaluators found built-in Chinese Communist Party censorship in older DeepSeek versions, but says the effect of this edit on political answers has not been measured. Those older-version findings do not establish how this version behaves.
Independent Tests Would Clarify Claims
The immediate next step is independent testing of the published model: researchers or users could assess its refusal behavior, reasoning performance and hardware needs against the publisher’s figures. Checks of the model files could also help establish what AtomicChat changed and what remained intact.
Until those results are available, the reported 0% refusal rate and single-GPU performance should be treated as publisher claims. The source material does not identify a planned independent audit or a scheduled follow-up from AtomicChat or DeepSeek.
Key Questions
What did AtomicChat publish?
It published an edited, compressed version of DeepSeek V4.1-Flash-1017 on Hugging Face. The build combines third-party compression work with AtomicChat’s refusal-removing edit.
Does it really run on one 12GB graphics card?
The developers say it can run on a single GPU with 12GB of VRAM. Their stated requirements also include 46–64GB of system RAM and about 250GB of downloads, with performance around 5–10 tokens per second. Those figures have not been independently replicated.
How was the 0% refusal figure measured?
AtomicChat reports a 0.0% refusal rate on JailbreakBench, down from 85.2% for the original model. The figures are self-reported, and the publisher says its classifier can miss a soft refusal later in an answer.
Does the edit remove political bias?
That has not been measured. The publisher says its edit removes refusals across topics, but the source material does not establish whether the model’s political slant changed.
Is the model’s use unrestricted?
The source says the base model is MIT-licensed and its model card has no acceptable-use policy. AtomicChat tells users they are responsible for their use and for following applicable laws; the license alone does not settle every legal or organizational obligation.
Source: Thorsten Meyer AI
