MODEL PROFILE · 模型档案

MiMo-V2.6-Distill-Qwen-9B-heretic

saidutta69 · MiMo-V2.6-Distill-Qwen-9B-heretic

A decensored variant of XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B, produced with Heretic v1.4.0 (directional ablation / "abliteration"). Xiaomi's MiMo-V2.6 distill is a 9B agentic model covering coding, visual coding, tool use, and security work; refusal behaviour is suppressed here via targeted weight edits to the attention output and MLP down-projections rather than fine-tuning, so the SFT'd agentic behaviour and the vision encoder are left largely intact. **Who this is for:** developers who want an uncensored agentic coding model - this is the one in this collection tuned for SWE-bench-style work, terminal and tool use, and visual coding, and the refusals here dropped from 99/100 to 8/100, the lowest absolute refusal count of this batch. MIT licensed, multimodal, 262K context, and it runs locally via GGUF on a 16 GB GPU. Best for local coding agents, code review, and security research workflows. Weights only, at this model's native 9B size; add ~1 GB per 32K of context. OOM? Drop one quant level. Headroom to spare? Go one up.

译文排队中,当前展示来源原文。

视觉问答codemultimodalreasoninglicense:mit
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 19:16:39
OVERVIEW

模型介绍

A decensored variant of XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B, produced with Heretic v1.4.0 (directional ablation / "abliteration"). Xiaomi's MiMo-V2.6 distill is a 9B agentic model covering coding, visual coding, tool use, and security work; refusal behaviour is suppressed here via targeted weight edits to the attention output and MLP down-projections rather than fine-tuning, so the SFT'd agentic behaviour and the vision encoder are left largely intact. **Who this is for:** developers who want an uncensored agentic coding model - this is the one in this collection tuned for SWE-bench-style work, terminal and tool use, and visual coding, and the refusals here dropped from 99/100 to 8/100, the lowest absolute refusal count of this batch. MIT licensed, multimodal, 262K context, and it runs locally via GGUF on a 16 GB GPU. Best for local coding agents, code review, and security research workflows. Weights only, at this model's native 9B size; add ~1 GB per 32K of context. OOM? Drop one quant level. Headroom to spare? Go one up.

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

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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

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。