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

Qwen3.8-27B-Unleashed-GGUF

outsourc-e · Qwen3.8-27B-Unleashed-GGUF

> ### ✅ All 9 tiers verified and live (2026-08-21) > > `UD-IQ1_M` and `UD-IQ2_S` were briefly published broken, then withdrawn, and are now > **rebuilt, load-tested, and back**. If you downloaded either before 2026-08-21 22:00 UTC, > re-download. No other tier was ever affected. > > **If you are quantizing this model yourself, this is the part worth reading.** Upstream > builds low-bit tiers with a *different architecture*, not just different bit widths: > > | | Q3_K_XL and above | IQ1_M / IQ2_S | > |---|---|---| > | tensors | 506 | 498 | > | highest block | 64 (MTP head at `q6_k`) | 63 (MTP pruned) | > | `block_count` | 65 | **64** | > | `nextn_predict_layers` | 1 | **0** | > > Their imatrix has 496 entries because it matches that pruned topology. Quantizing a > 65-block f16 against it fails with > `Missing importance matrix for tensor blk.64.attn_k.weight`. > > `--prune-layers 64` alone is **not** enough — it removes the tensors but leaves the model > declaring an MTP head, so loading dies on `blk.63.nextn.eh_proj.weight not found`: a > structurally valid file containing an unloadable model. You also have to rewrite the > metadata: > > > > Every file here is now gated on exit code → magic bytes → **an actual load test** before > upload. Header checks alone let two broken builds through; only loading the model catches it. Most uncensored GGUFs are uniform quants — every layer gets the same treatment. These use a **per-tensor type map**, so sensitive tensors keep precision while tolerant ones compress hard. The result: a **Q3 that outperforms a Q4 3.3 GB larger**, with full 250k-token retrieval. > **Provenance, stated plainly.** The per-tensor quant map was **extracted from > unsloth/Qwen3.8-27B-GGUF** > (Unsloth Dynamic 3.0) by reading the > tensor-type table from their published files, then applied to > JonathanColetti/Qwen3.8-27B-Uncensored > weights with that repo's imatrix. **The recipe is Unsloth's work, not ours** — we did not > independently derive it. All upstream projects are Apache 2.0. See Credits.

翻訳待ちのため原文を表示しています。

テキスト生成license:apache-2.0
コンテキスト長非公開
入力 → 出力テキスト → テキスト
モデルウェイト未確認
情報更新日時2026/11/2 19:15:42
OVERVIEW

モデル紹介

> ### ✅ All 9 tiers verified and live (2026-08-21) > > `UD-IQ1_M` and `UD-IQ2_S` were briefly published broken, then withdrawn, and are now > **rebuilt, load-tested, and back**. If you downloaded either before 2026-08-21 22:00 UTC, > re-download. No other tier was ever affected. > > **If you are quantizing this model yourself, this is the part worth reading.** Upstream > builds low-bit tiers with a *different architecture*, not just different bit widths: > > | | Q3_K_XL and above | IQ1_M / IQ2_S | > |---|---|---| > | tensors | 506 | 498 | > | highest block | 64 (MTP head at `q6_k`) | 63 (MTP pruned) | > | `block_count` | 65 | **64** | > | `nextn_predict_layers` | 1 | **0** | > > Their imatrix has 496 entries because it matches that pruned topology. Quantizing a > 65-block f16 against it fails with > `Missing importance matrix for tensor blk.64.attn_k.weight`. > > `--prune-layers 64` alone is **not** enough — it removes the tensors but leaves the model > declaring an MTP head, so loading dies on `blk.63.nextn.eh_proj.weight not found`: a > structurally valid file containing an unloadable model. You also have to rewrite the > metadata: > > > > Every file here is now gated on exit code → magic bytes → **an actual load test** before > upload. Header checks alone let two broken builds through; only loading the model catches it. Most uncensored GGUFs are uniform quants — every layer gets the same treatment. These use a **per-tensor type map**, so sensitive tensors keep precision while tolerant ones compress hard. The result: a **Q3 that outperforms a Q4 3.3 GB larger**, with full 250k-token retrieval. > **Provenance, stated plainly.** The per-tensor quant map was **extracted from > unsloth/Qwen3.8-27B-GGUF** > (Unsloth Dynamic 3.0) by reading the > tensor-type table from their published files, then applied to > JonathanColetti/Qwen3.8-27B-Uncensored > weights with that repo's imatrix. **The recipe is Unsloth's work, not ours** — we did not > independently derive it. All upstream projects are Apache 2.0. See Credits.

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 · outsourc-e/Qwen3.8-27B-Unleashed-GGUF ↗2026/11/1 00:00:31
huggingface · enrich:2f061639-7b1d-4fc5-b611-a0fe37591739 ↗2026/10/29 19:10:16

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。