DeepSeek-V4.1-Flash-UNCENSORED-GGUF

模型介绍

**Uncensored 552B MoE with 1M-token context, vision, and full tool/reasoning support — converted to GGUF for llama.cpp.** Built from `dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8` (permanent weight-level abliteration of `deepseek-ai/DeepSeek-V4.1-Flash`), converted to GGUF and requantized. **No guardrails: the model follows on prompts the base model refuses (verified, see Benchmarks).** > ## ✅ Update 2026-10-15 — corrected re-quants are live > > **The Q3_K_M and Q4_K_M files on the Hub have been replaced** with corrected re-quants built from the Q8_0 bridge using the corrected per-layer ramp (see below). If you downloaded Q3 or Q4 earlier, the files have changed — re-download. > > - **Q3_K_M:** now **352 GB (9 × ≤50 GB shards)** — 23 GB smaller than the previous build, identical measured quality at every benchmark we ran on both. > - **Q4_K_M:** now **426 GB (3 shards)** — routed experts are pure Q4_K as the tier name says (the previous build re-encoded the checkpoint-native MXFP4 experts into Q4_K/Q5_K, which is what the audit flagged). > - **Q2_K:** rebuilt (v3) — now pins the indexer/hyperconn layers per the recipe (the previous build left them at base precision). Same size class, files replaced on the Hub. The benchmark table below is measured on v3; the 1M NIAH tier on v3 is running. > - **Q8_0:** unchanged — still the reference bridge. > > Details: Per-group quantization & size · Benchmarks

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

常见问题

资料来源与可追溯记录

许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。