MODEL PROFILE · 模型档案

Qwopus3.6-27B-v2-MTP-GGUF

Jackrong · Qwopus3.6-27B-v2-MTP-GGUF

💡 What is Qwopus3.6-27B-v2-MTP? 🪐 Qwopus3.6-27B-v2-MTP is a speed-oriented reasoning release built on top of Qwen3.6-27B . It keeps the Qwopus line's focus on reconstructed reasoning traces, coding discipline, DevOps procedures, and mathematical derivations, while adding Multi-Token Prediction for faster generation. The goal is simple: preserve the depth and structure of a 27B reasoning model while making real interactive use noticeably faster. ⚡ MTP Decoding Auxiliary future-token prediction improves throughput on long reasoning, code, math, and strict-format prompts. 🧩 Structured Reasoning Inherits the Qwopus training recipe built around reconstructed step-by-step reasoning trajectories. 🧪 GB10 Tested Validated on a 30-question local benchmark across Logic, Coding, DevOps, Math, and Edge tasks. 🚀 Practical Speed Designed for workflows where strong answers matter, but waiting several extra minutes per task does not. Qwen3.6-27B provides the dense 27B foundation for this release. Qwopus3.6-27B-v2-MTP focuses on preserving the base model's broad reasoning capability while tuning the output style toward stepwise analysis, tool-aware execution, and practical engineering answers. Attribute Specifications & Details 🧠 Architecture Dense Transformer / 27 Billion Parameters 🎯 Focus Domains Agentic Coding, DevOps, structured logic, mathematics, and strict-format output ⚡ MTP Objective Improve generation throughput through multi-token speculative prediction while retaining final-answer quality.

译文排队中,当前展示来源原文。

视觉问答multimodalreasoningcoderlicense:apache-2.0
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 19:15:30
OVERVIEW

模型介绍

💡 What is Qwopus3.6-27B-v2-MTP? 🪐 Qwopus3.6-27B-v2-MTP is a speed-oriented reasoning release built on top of Qwen3.6-27B . It keeps the Qwopus line's focus on reconstructed reasoning traces, coding discipline, DevOps procedures, and mathematical derivations, while adding Multi-Token Prediction for faster generation. The goal is simple: preserve the depth and structure of a 27B reasoning model while making real interactive use noticeably faster. ⚡ MTP Decoding Auxiliary future-token prediction improves throughput on long reasoning, code, math, and strict-format prompts. 🧩 Structured Reasoning Inherits the Qwopus training recipe built around reconstructed step-by-step reasoning trajectories. 🧪 GB10 Tested Validated on a 30-question local benchmark across Logic, Coding, DevOps, Math, and Edge tasks. 🚀 Practical Speed Designed for workflows where strong answers matter, but waiting several extra minutes per task does not. Qwen3.6-27B provides the dense 27B foundation for this release. Qwopus3.6-27B-v2-MTP focuses on preserving the base model's broad reasoning capability while tuning the output style toward stepwise analysis, tool-aware execution, and practical engineering answers. Attribute Specifications & Details 🧠 Architecture Dense Transformer / 27 Billion Parameters 🎯 Focus Domains Agentic Coding, DevOps, structured logic, mathematics, and strict-format output ⚡ MTP Objective Improve generation throughput through multi-token speculative prediction while retaining final-answer quality.

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: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。