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AIモデルライブラリ/prithivMLmods/Qwen3.8-27B-GGUF
モデル情報

Qwen3.8-27B-GGUF

prithivMLmods · Qwen3.8-27B-GGUF

> **Qwen3.8-27B** is a 27-billion-parameter dense causal language model with a native vision encoder from the Qwen team, built on the Qwen3.5 architectural foundation as a compact, deployment-friendly member of the newly introduced Qwen3.8 generation — the most capable in the Qwen open-model family to date. Its 64-layer hybrid architecture interleaves Gated DeltaNet linear-attention blocks with periodic Gated Attention layers, trained with Multi-Token Prediction, and supports a native 262,144-token context window (extensible to 1M via YaRN scaling), native image and video understanding from STEM diagrams to hour-scale videos, and flexible thinking control via a `reasoning_effort` parameter (xhigh/medium/low) with thinking enabled by default and historical reasoning preserved across turns. It delivers substantial gains over its predecessor Qwen3.6-27B and often rivals or exceeds larger models like Muse Glimmer-30B and even Opus 4.6 Max on several benchmarks — scoring 73.0 on Terminal-Bench 2.1, 61.7 on SWE-bench Pro, 84.3 on OSWorld-Verified computer-use, 81.9 on AndroidWorld mobile-use, and 90.3 on LiveCodeBench v6 — reflecting particular strength in agentic coding, computer/browser/mobile-use tasks, and multimodal tool use, while remaining competitive on general reasoning benchmarks like GPQA Diamond (89.2) and IFBench (79.5). It's compatible with Hugging Face Transformers, vLLM, SGLang, and TokenSpeed out of the box, released under Apache-2.0. > [!NOTE] Multi-Token Prediction (MTP) GGUF is a specialized GGUF model file format extension that integrates speculative decoding directly into the model weights to significantly accelerate local inference. Unlike traditional speculative decoding which requires a separate, smaller "draft" model, MTP GGUF files include additional output heads within the main model architecture that predict multiple future tokens in a single forward pass. File Name | Quant Type | File Size | File Link | |-----------|------------|-----------|-----------| | Qwen3.8-27B.BF16.gguf | BF16 | 54.7 GB | Download | | Qwen3.8-27B.F16.gguf | F16 | 54.7 GB | Download | | Qwen3.8-27B.Q2_K.gguf | Q2_K | 10.9 GB | Download | | Qwen3.8-27B.Q3_K_L.gguf |

翻訳待ちのため原文を表示しています。

画像質問応答multimodallicense: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2026/10/30 06:42:51
OVERVIEW

モデル紹介

> **Qwen3.8-27B** is a 27-billion-parameter dense causal language model with a native vision encoder from the Qwen team, built on the Qwen3.5 architectural foundation as a compact, deployment-friendly member of the newly introduced Qwen3.8 generation — the most capable in the Qwen open-model family to date. Its 64-layer hybrid architecture interleaves Gated DeltaNet linear-attention blocks with periodic Gated Attention layers, trained with Multi-Token Prediction, and supports a native 262,144-token context window (extensible to 1M via YaRN scaling), native image and video understanding from STEM diagrams to hour-scale videos, and flexible thinking control via a `reasoning_effort` parameter (xhigh/medium/low) with thinking enabled by default and historical reasoning preserved across turns. It delivers substantial gains over its predecessor Qwen3.6-27B and often rivals or exceeds larger models like Muse Glimmer-30B and even Opus 4.6 Max on several benchmarks — scoring 73.0 on Terminal-Bench 2.1, 61.7 on SWE-bench Pro, 84.3 on OSWorld-Verified computer-use, 81.9 on AndroidWorld mobile-use, and 90.3 on LiveCodeBench v6 — reflecting particular strength in agentic coding, computer/browser/mobile-use tasks, and multimodal tool use, while remaining competitive on general reasoning benchmarks like GPQA Diamond (89.2) and IFBench (79.5). It's compatible with Hugging Face Transformers, vLLM, SGLang, and TokenSpeed out of the box, released under Apache-2.0. > [!NOTE] Multi-Token Prediction (MTP) GGUF is a specialized GGUF model file format extension that integrates speculative decoding directly into the model weights to significantly accelerate local inference. Unlike traditional speculative decoding which requires a separate, smaller "draft" model, MTP GGUF files include additional output heads within the main model architecture that predict multiple future tokens in a single forward pass. File Name | Quant Type | File Size | File Link | |-----------|------------|-----------|-----------| | Qwen3.8-27B.BF16.gguf | BF16 | 54.7 GB | Download | | Qwen3.8-27B.F16.gguf | F16 | 54.7 GB | Download | | Qwen3.8-27B.Q2_K.gguf | Q2_K | 10.9 GB | Download | | Qwen3.8-27B.Q3_K_L.gguf |

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

huggingface · prithivMLmods/Qwen3.8-27B-GGUF ↗2026/10/29 18:39:32
huggingface · enrich:8f84f857-2470-45b3-9b22-aa1b54fc2e34 ↗2026/10/30 06:42:51

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。