DeepSeek V4.1 Flash 1017 Uncensored

• 🔒 Abliterated build of DeepSeek V4.1 Flash 1017, refusal behavior removed
• 🧠 Multimodal Mixture-of-Experts with a 552B-parameter backbone
• 📏 Native one-million-token context window preserved from upstream
• 👁️ Processes images and text natively, plus video per model card
• 🔧 Tool calling, reasoning-effort tiers and speculative decoding retained
• 🆕 Based on DeepSeek's 1017 refresh of V4.1 Flash
• ⚡ Served in FP8; further low-bit quantizing may regress compliance
• 📚 MIT-licensed open weights, published by dealignai on Hugging Face

DeepSeek

DeepSeek is a Chinese artificial intelligence company specializing in large language model development, founded in July 2023 by Liang Wenfeng. Based in Hangzhou, Zhejiang, the company is backed by High-Flyer, a prominent Chinese hedge fund also co-founded by…

DeepSeek V4.1 Flash 1017 Uncensored is a third-party, "abliterated" derivative of DeepSeek's own DeepSeek v4.1 Flash (1017), published by dealignai as an FP8 checkpoint. The edit targets refusal behavior at the weight level so the model answers across topic categories rather than declining; according to the model card, everything else from upstream is preserved — vision, video, reasoning-effort tiers, tool calling, speculative decoding and the native one-million-token context.

The underlying base matters for the generational story. DeepSeek describes the 1017 release as a minor update to DeepSeek V4.1 Flash that delivers improvements across reasoning, coding, agentic tasks and visual benchmarks. Architecturally it remains a multimodal Mixture-of-Experts with a 552B-parameter backbone, generating text autoregressively from image and text inputs.

Relative to the earlier uncensored sibling DeepSeek V4.1 Flash Uncensored, this build tracks the newer 1017 weights rather than the original V4.1 Flash release. Lineage-wise, the Flash line descends from earlier releases such as DeepSeek V4 Flash 0731 and DeepSeek V3.2.

Two practical caveats from the publisher: abliteration alters the base model's alignment surgically, so outputs warrant more scrutiny, and naively re-quantizing the FP8 weights to lower bit widths stacks quantization error on top of the edit, which can degrade behavior.