AI NEWS.ccAI正在发生 未来正在更新
♡ 我的收藏 0
AI 模型库/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-8bit
MODEL PROFILE · 模型档案

Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-8bit

AEON-7 · Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-8bit

> [!IMPORTANT] > **Superseded by Qwen3.8 — use this instead:** `AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-NVFP4-MIXED` > > **Operator recipes (Spark Dynamic DFlash lattice, TP=2, RTX):** `AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED` > > This Qwen3.6-27B card stays online for history and existing downloads. For new work, move to the Qwen3.8 Ultimate Uncensored NVFP4-MIXED release. That line is far superior in capability and reaches substantially more of what the architecture can do, with a more refined mixed quantization methodology (NVFP4 + FP8 lattice) than these older Qwen3.6 exports. > > Serve with the unified container: `ghcr.io/aeon-7/aeon-vllm-ultimate:latest` (Spark / GB10) or `ghcr.io/aeon-7/aeon-vllm-ultimate-rtx:latest` (RTX discrete). > **The flagship, max-fidelity Apple-Silicon build** of `AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16`. Affine **8-bit** (group-64) on the compressible bulk of the hybrid decoder, and **BF16** on the parts that don't tolerate it: the Gated-DeltaNet / Mamba state dynamics, the full vision tower, and the in-weights MTP head. Built and validated on a **MacBook Pro M4 Pro (48 GB)**. > > **Target hardware: Apple Silicon (M-series), 36 GB+ unified memory** (peaks 29.85 GB). Full multimodal (text + image) via `mlx-vlm`. > > Want a smaller, faster build? See the compact sibling: `…-Multimodal-MLX-FP4` (16 GB, 1.85× faster decode). This is the **fidelity** member of the MLX quant grid (29.5 GB on disk, 8.634 bpw) — the tightest match to the BF16 source you can run natively on Apple Silicon. Affine 8-bit is near-lossless on the residual-writers, and the recipe keeps the numerically fragile **Gated-DeltaNet** recurrence, the entire **vision tower**, and the **MTP head** in BF16 so the hybrid SSM, multimodal path, and self-speculation all stay intact. For a tight unified-memory budget (24 GB+ Macs) at ~3.2× the single-stream throughput, the compact **MLX-FP4** sibling is one click away.

译文排队中，当前展示来源原文。

视觉问答multimodallicense:other
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 19:15:46
OVERVIEW

模型介绍

> [!IMPORTANT] > **Superseded by Qwen3.8 — use this instead:** `AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-NVFP4-MIXED` > > **Operator recipes (Spark Dynamic DFlash lattice, TP=2, RTX):** `AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED` > > This Qwen3.6-27B card stays online for history and existing downloads. For new work, move to the Qwen3.8 Ultimate Uncensored NVFP4-MIXED release. That line is far superior in capability and reaches substantially more of what the architecture can do, with a more refined mixed quantization methodology (NVFP4 + FP8 lattice) than these older Qwen3.6 exports. > > Serve with the unified container: `ghcr.io/aeon-7/aeon-vllm-ultimate:latest` (Spark / GB10) or `ghcr.io/aeon-7/aeon-vllm-ultimate-rtx:latest` (RTX discrete). > **The flagship, max-fidelity Apple-Silicon build** of `AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16`. Affine **8-bit** (group-64) on the compressible bulk of the hybrid decoder, and **BF16** on the parts that don't tolerate it: the Gated-DeltaNet / Mamba state dynamics, the full vision tower, and the in-weights MTP head. Built and validated on a **MacBook Pro M4 Pro (48 GB)**. > > **Target hardware: Apple Silicon (M-series), 36 GB+ unified memory** (peaks 29.85 GB). Full multimodal (text + image) via `mlx-vlm`. > > Want a smaller, faster build? See the compact sibling: `…-Multimodal-MLX-FP4` (16 GB, 1.85× faster decode). This is the **fidelity** member of the MLX quant grid (29.5 GB on disk, 8.634 bpw) — the tightest match to the BF16 source you can run natively on Apple Silicon. Affine 8-bit is near-lossless on the residual-writers, and the recipe keeps the numerically fragile **Gated-DeltaNet** recurrence, the entire **vision tower**, and the **MTP head** in BF16 so the hybrid SSM, multimodal path, and self-speculation all stay intact. For a tight unified-memory budget (24 GB+ Macs) at ~3.2× the single-stream throughput, the compact **MLX-FP4** sibling is one click away.

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/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-8bit ↗2026/10/30 19:13:01
huggingface · enrich:50e5c6ec-a475-4e37-83e0-1e2fc85b3474 ↗2026/10/31 12:20:54

许可证：other。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。