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AI 模型库/AEON-7/Gemma-4-12B-it-AEON-Abliterated-MLXFP4
MODEL PROFILE · 模型档案

Gemma-4-12B-it-AEON-Abliterated-MLXFP4

AEON-7 · Gemma-4-12B-it-AEON-Abliterated-MLXFP4

> **The compact, fast, 16 GB-friendly Apple-Silicon build** of `AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16`. True 4-bit `mxfp4` on the bulk of the decoder, **8-bit `mxfp8` on the two residual-writers that carry the abliteration edit**, and **bf16** on the soft-capped head and the vision/audio projectors. Built and validated on a **MacBook Pro M4 Pro (48 GB)**. > > **Target hardware: Apple Silicon (M-series), runs on 16 GB+ unified memory** (peaks ~10.1 GB). Full multimodal (text + image + audio) via `mlx-vlm`. > > Want maximum fidelity? See the near-lossless 8-bit sibling: `…-MLX-8bit`. This is the **smallest** member of the MLX quant grid (9.3 GB) and the **fastest single-stream** — a high-quality compact build for Apple Silicon on a tight unified-memory budget. It stays fully coherent, keeps the abliteration intact (**0/8 refusals on the harmful probe**), and preserves the full vision/audio path. For the tightest possible match to BF16, the near-lossless **MLX-8bit** sibling is one click away. **0 → running on a fresh Mac** (no Python, no tools needed) — `uv` installs a correct Python + the deps for you:

译文排队中，当前展示来源原文。

视觉问答multimodalreasoninglicense:gemma
上下文窗口来源未披露
输入 → 输出文本、图像 → 文本
模型权重待核验
档案更新时间2026/11/2 19:15:31
OVERVIEW

模型介绍

> **The compact, fast, 16 GB-friendly Apple-Silicon build** of `AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16`. True 4-bit `mxfp4` on the bulk of the decoder, **8-bit `mxfp8` on the two residual-writers that carry the abliteration edit**, and **bf16** on the soft-capped head and the vision/audio projectors. Built and validated on a **MacBook Pro M4 Pro (48 GB)**. > > **Target hardware: Apple Silicon (M-series), runs on 16 GB+ unified memory** (peaks ~10.1 GB). Full multimodal (text + image + audio) via `mlx-vlm`. > > Want maximum fidelity? See the near-lossless 8-bit sibling: `…-MLX-8bit`. This is the **smallest** member of the MLX quant grid (9.3 GB) and the **fastest single-stream** — a high-quality compact build for Apple Silicon on a tight unified-memory budget. It stays fully coherent, keeps the abliteration intact (**0/8 refusals on the harmful probe**), and preserves the full vision/audio path. For the tightest possible match to BF16, the near-lossless **MLX-8bit** sibling is one click away. **0 → running on a fresh Mac** (no Python, no tools needed) — `uv` installs a correct Python + the deps for you:

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。

PRICING & COST

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。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价，请查阅来源渠道。

按月估算基础 Token 费用

—

预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

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。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用？

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗？

不是实时结算价格，而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

huggingface · AEON-7/Gemma-4-12B-it-AEON-Abliterated-MLXFP4 ↗2026/10/30 18:19:43
huggingface · enrich:aefda0ca-8298-4c7f-be43-c7323572006b ↗2026/10/31 10:35:29

许可证：gemma。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。