モデル情報

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.

翻訳待ちのため原文を表示しています。

画像質問応答multimodalreasoningcoderlicense:apache-2.0
コンテキスト長非公開
入力 → 出力テキスト、画像 → テキスト
モデルウェイト未確認
情報更新日時2026/11/2 19:15:30
OVERVIEW

モデル紹介

💡 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.

主な特徴

特点摘要待依据来源补齐,不凭模型名称推断能力。

制約と注意点

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

PRICING & COST

出典別料金と費用分析

料金はチャネル・地域・設定によって異なります。各チャネルの料金は個別に表示し、提供元の統一公式料金とは見なしません。

出典・チャネル入力出力課金単位収集日時
確認可能な料金がありません。出典を確認してください。

月額の基本トークン費用を試算

—

試算はキャッシュ割引、段階料金、マルチモーダル追加料金、検索、税金、追加推論出力を含みません。

INDEPENDENT BENCHMARKS

公開評価と実行設定

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。

収録済みの全ランキング →

よくある質問

オープンウェイトなら無料で商用利用できますか?

必ずしもそうではありません。バージョン別のライセンス、商用条件、利用方針を確認してください。自前運用には計算資源と保守費用も必要です。

これはリアルタイムの請求料金ですか?

収集日時付きの料金スナップショットです。最新のバージョン、地域、割引、課金方式は各チャネルで確認してください。

出典と追跡記録

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。