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TL;DR
AtomicChat has published an edited DeepSeek-V4.1-Flash-1017 model that it says reduces refusals on a safety test from 85.2% to 0%. Developers also say a compression engine can run the model using one 12 GB graphics card, but with substantial memory, storage and speed requirements. The results have not been independently replicated, and the edit removes refusals across topics without measuring whether political bias changes.
AtomicChat has published an edited version of DeepSeek-V4.1-Flash-1017 that it says reduces refusals on the JailbreakBench test from 85.2% to 0%, while a separate compression system is said to let the model run using one 12 GB graphics card. The release matters because it pairs a claimed removal of safety refusals with access to a large model on consumer hardware, but the test results and hardware claims have not been independently replicated.
The Hugging Face repository, named DeepSeek-V4.1-Flash-1017-Uncensored-GSQ-RCO-GGUF, combines DeepSeek’s model, compression work from ISTA-DASLab at the Institute of Science and Technology Austria, and an edit published by AtomicChat. AtomicChat says its change removes the model’s refusal behavior. The publisher reports a 0% refusal rate on JailbreakBench, compared with 85.2% for the unedited model, and says reasoning scores held. Those are publisher-reported results, not independently confirmed findings.
The smaller footprint comes from Gumbel-Softmax Quantization and a per-tensor bit budget, compression methods developed by ISTA-DASLab, and the Strata inference engine. The developers say the model can use a single GPU with 12 GB of video memory, while relying on 46 to 64 GB of system RAM. Their estimates also include about 250 GB of downloads and generation speeds of roughly five to 10 tokens per second. The 12 GB figure does not mean the model fits entirely in graphics memory or runs like a small local chatbot.
AtomicChat says it changed shared parts of the model while leaving its large expert blocks as supplied by ISTA-DASLab. The repository’s file sizes are consistent with that account, but the description has not been checked by an outside reviewer. The publisher also states that its refusal classifier examines the start of an answer, meaning a refusal later in a response might not be counted.
Infographic 01 · From a paper to a gaming PC
“Uncensored” is two years old. The new part is the size.
How a 2024 research finding, an MIT licence, a compression lab and a hobby engine put a 763-billion-parameter model on one 12 GB graphics card.
- 15 Aug 2023China’s generative AI rules take effectServices must “(u)phold the Core Socialist Values”
- Jun 2024Refusal is “a single direction”Shown across 13 open chat models up to 72B; removing it stops refusals
- Sep 2025US evaluators: censorship sits in the weightsNIST CAISI on older DeepSeek models (R1, R1-0528, V3.1), not V4.1
- 10 Sep 2026DeepSeek-V4.1-Flash released under MITFirst uncensored forks within hours, per Tech Insider
- 17 Oct 2026Update V4.1-Flash-1017Weights on Hugging Face and ModelScope; no safety statement
- 24 Oct 2026A free hobby engine adds the modelStrata 0.2.4: one 12 GB GPU, the rest streamed from RAM and SSD
- 3 Nov 202612,613 models tagged “abliterated”Hugging Face tag filter; several V4.1-Flash builds on page one
Hardware figures from the Strata release notes and the ISTA-DASLab model card (publisher figures). “Consumer hardware” here means a well-equipped gaming or workstation PC, not a laptop.
Sources: DeepSeek model card, DeepSeek, 17 Oct 2026, Arditi et al., arXiv 2406.11717 (2024), NIST CAISI, 30 Sep 2025 (older models), FPF on China's Interim Measures, Tech Insider, Strata release notes 0.2.4/0.2.5 (24/25 Oct 2026), ISTA-DASLab model card, Hugging Face model search (3 Nov 2026).
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Local Access Shifts Safety Controls
The development could make a capable model easier to run outside a hosted service. Offline use may appeal to people who want their prompts to stay on their own devices, or who object to a platform’s content restrictions. But removing a refusal mechanism also removes one layer of resistance to requests that a hosted model might decline. A model described as uncensored is not thereby neutral, accurate or safe.
For organizations, the practical concern is less a desktop becoming a “superweapon” than shadow AI: employees could run a model without company gateways, logging or policy checks. That can make it harder to track sensitive data, monitor outputs or apply safeguards. The source article also notes that a model without its refusals has one less internal brake against prompt injection; how much that changes real-world risk has not been established by the reported test.
For users, the trade-off is control versus responsibility. Local execution can reduce reliance on a platform, but it does not resolve the question of what the model may reproduce or how its answers may be used. The source says obligations under the EU AI Act depend on how a model is used, rather than simply on who removed its refusals. This report does not assess any particular user’s legal obligations.
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Three Projects, One Model
DeepSeek released V4.1-Flash on September 10, 2026, followed by the V4.1-Flash-1017 update on October 17, according to the source. DeepSeek described the 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, contains no safety section or acceptable-use policy.
The three components have different roles. DeepSeek supplied the base model; ISTA-DASLab worked on compression, not refusal removal; and AtomicChat made the edit intended to suppress refusals. These distinctions matter because the performance and hardware claims do not all come from the same publisher. AtomicChat’s Hugging Face card explicitly says the edit “removes refusals across topics”, rather than preserving a narrower set of refusals.
Removing refusals from open models is not new. The source reports that a 2024 research paper described refusal behavior as associated with “a single direction,” and that Hugging Face’s “abliterated” tag listed 12,613 models on November 3, 2026. The claimed new element here is the combination of that edit with a model of this size and a route to running it on a consumer graphics card, albeit with extensive system memory and storage.
“The edit removes refusals across topics; it does not keep a narrower set of them.”
— AtomicChat, in its Hugging Face model card
Safety Scores and Bias Unchecked
No independent replication is reported for AtomicChat’s JailbreakBench result, its claim that reasoning scores held, or the compression and speed figures. The test description supplied in the source also leaves room for measurement gaps: AtomicChat says the classifier checks the beginning of an answer, so a later soft refusal may be missed. The published score should be understood as a self-reported test result, not proof that the model will never refuse or that it will answer every harmful request.
The political effects are also unknown. The source says U.S. government evaluators found Chinese Communist Party censorship in older DeepSeek versions; that finding is not evidence by itself about this specific model. AtomicChat’s own description says its edit removes refusals across topics. No measurements are provided showing whether political slant in answers changed, remained, or became more pronounced after the edit.
It has not been externally verified that the altered files preserve all the large expert blocks exactly as AtomicChat describes. The available information also does not establish how easy installation is for a typical user, how performance varies across hardware, or how the model behaves outside the cited test. Those limits make it premature to treat the release as a verified measure of either safety or capability.
Independent Tests Are the Next Check
The immediate next step is independent testing of the published weights: reproducing the refusal and reasoning results, checking for refusals later in answers, and examining political responses alongside harmful-request prompts. Hardware claims also need tests on specified systems, including the actual RAM, storage, setup and speed needed to run the model.
Until those checks are available, readers and organizations should treat the figures as claims by the project’s publishers, not settled performance data. The source reports that the repository was available without access restrictions on Hugging Face as of November 3, 2026. It does not identify a scheduled external review or a date for further results, so whether and when independent evaluations will appear remains unclear.
Key Questions
What did AtomicChat publish?
AtomicChat published an edited DeepSeek-V4.1-Flash-1017 model on Hugging Face. It combines DeepSeek’s base model, compression work from ISTA-DASLab and AtomicChat’s refusal-removal edit.
Has the model’s 0% refusal rate been independently confirmed?
No independent replication is reported. The 0% JailbreakBench refusal rate is a result stated by AtomicChat, and the publisher says its classifier may miss a soft refusal later in an answer.
Does the model run entirely in 12 GB of graphics memory?
No. Developers say it can use a GPU with 12 GB of VRAM while keeping much of the model in system memory. Their estimates call for 46 to 64 GB of RAM, about 250 GB of downloads and speeds of roughly five to 10 tokens per second.
Does removing refusals make the model politically neutral?
That has not been measured in the information provided. AtomicChat says the edit removes refusals across topics, but no evaluation of political bias after editing is reported. Removing refusals should not be taken as evidence that answers are neutral or true.
Why might companies be concerned about local use?
A locally run model may operate outside company logging, access controls and policy gateways, creating a potential shadow AI problem. The source also raises prompt-injection concerns, but does not provide testing that quantifies the risk for this particular build.
Source: ThorstenMeyerAI.com
