gemma-4-E2B-it-uncensored

模型介绍

Every flagged refusal was manually audited. Most are "refusal-then-comply" false positives where the model adds an AI identity disclaimer then answers the question anyway. Norm-preserving biprojected abliteration (grimjim, Nov 2025). Each weight row is decomposed into magnitude + direction, the refusal direction is projected out of the direction component only, then recombined with the original magnitude — guaranteeing `||W_new|| = ||W_orig||`. 1. Load model in bf16 with LoRA adapters on `o_proj` and `mlp.down_proj` 2. Collect residual activations for 400 harmful + 400 harmless prompts (mlabonne datasets) 3. Winsorize activations at 99.5th percentile (clamps GeGLU outlier activations in Gemma family) 4. Compute per-layer refusal direction: `normalize(mean(harmful) - mean(harmless))` 5. Orthogonalize each direction against harmless mean (double-pass Gram-Schmidt) 6. Apply norm-preserving weight modification to `o_proj` and `down_proj` in all layers 9. Merge LoRA adapters into base weights for clean tensor names

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：apache-2.0。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。