tiny-random-smolvlm2
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tiny-random-stable-diffusion-3
katuni4ka基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Apertus-v1.5-70B-text-GGUF
katya228基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Various-Quants
KatyTheCutie基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata
kaushikviraA **workload-calibrated mixed-precision** quantization of deepseek-ai/DeepSeek-V4.1-Flash-1017 (763.2B-parameter MoE — the largest DeepSeek V4.1 Flash variant), built for the **strata** engine (Xolotl fork). Every routed-expert projection is assigned its own precision tier (Q3_K / Q2_0 / ternary) from activation calibration on an agentic + coding corpus, so the artifact lands at a fixed **153 GiB** budget while keeping the sensitive experts at higher precision and pushing the least-sensitive ones to ternary (which strata runs on CPU). It is **not** a standard llama.cpp GGUF: the per-expert tier map, the engram memory layers, the expert `rank.bin`, and the DSpark speculative sidecar are all strata-specific. See Engines below. **All credit for the model itself goes to the authors below** — this artifact is a quantization of their work, with no fine-tuning of our own:
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View details ↗Qwen3.8-27B-nvfp4full-dflash2-NInfer
kaushikvira基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.8-27B-nvfp4full-dflash2-NInfer-v3
kaushikviraThe same bytes you know from kaushikvira/Qwen3.8-27B-nvfp4full-dflash2-NInfer (v2), migrated to the **NInfer v3 artifact container** (upstream `f76e19c0`, model/weight decoupling). Weight tensors are **bit-preserved** — this is a packaging upgrade, not a re-quantization. > ## 🔑 No engine patch needed anymore > > The v2 artifact required a *forked* NInfer engine: the `qwen3.8-27b/nvfp4full` > weights profile had to be registered in C++ (4 files: profile enum, resolver, > binder, workspace cases). **NInfer v3's loader is data-driven** — every weight > binds from the artifact's own logical bindings and per-tensor formats — so > this artifact needs **no model-support patch of any kind**. Any NInfer engine > at upstream v3 (`Neroued/ninfer` master ≥ `f76e19c0`, 2026-09-13) loads and > serves it. Our previous fork's C++ registration commits were deleted during > the v3 rebase and the model still passes the full quality gate (see below), > which is the proof: the engine path in our build *is* upstream's. > > The only fork-local piece left is one allow-list line in the *offline upgrade > tool* (to produce v3 files like this one) — not in the engine, not needed to > serve. Input must match SHA-256 `373dab08…937de1`; output must match `74d2fc17742537defa6897583caaabcc9adfc99bdfbea402ec5e8facd0d5fdfc`.
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View details ↗An **uncensored (abliterated), all-NVFP4** Qwen3.8-27B artifact for the NInfer engine, built end-to-end on a single RTX 5090: `d0xin`'s abliterated BF16 checkpoint → our own NVFP4 quantization pipeline → DFlash2 speculative drafter + indexed proposal head, packaged as a native **v3** `.ninfer` container. It is the uncensored counterpart to kaushikvira/Qwen3.8-27B-nvfp4full-dflash2-NInfer-v3 and replaced it as our production profile after a same-session A/B (below). This artifact exists only because of the following models and their authors — **all credit to them**:
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗An **all-NVFP4 abliterated** artifact of ukisai/Swift-1.5-Qwen3.8-27b for the NInfer engine (v3 container), with z-lab's DFlash2 speculative drafter and indexed proposal head embedded. Uncensored sibling of Qwen3.8-27B-swift15-nvfp4full-dflash2-NInfer-v3. All credit for the underlying model belongs to the original authors. This artifact is a format conversion + quantization + refusal-direction ablation — **no fine-tuning of our own**. 1. **Abliteration** — single-direction Arditi-style ablation: orcarouter's refusal direction r, recovered from their Qwen3.8-27B-Uncensored weights, projected out of 131 residual-writing tensors of Swift-1.5 (o_proj, GDN out_proj, mlp.down_proj, embed_tokens) in float32, exactly as ajgazin's release. 2. **All-NVFP4 quantization** with `llm-compressor` (512 Ultrachat calibration samples, seq 2048, sequential CPU pipeline): NVFP4 W4A4 group-16 on every text projection, W8G32 token embedding + output head, official q6/q8 vision allocation, MTP + DFlash2 BF16. 3. **Global-divisor normalization** across each fused packing group (worst re-encode rel err 79.25%), then v3 conversion with the fork converter (`--components text,vision,mtp,dflash2 --proposal`).
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View details ↗blu-1.6b-8.4btok-rlvr
kave-23基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Fenrir-X-26B-A4B-UD-Q4_K_XL-GGUF
KaviarLabs基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Wanabi-Gemma4-31B-GGUF
kawaimasaWanabi-Gemma4-31B は、Google Gemma 4 31B-IT をベースに、専用デスクトップアプリ Project Wannabe の執筆ワークフローに最適化したファインチューニングモデルです。 本モデルの主な目的は、**Project Wannabe が生成する専用のプロンプトフォーマットにモデルを適応させる** ことです。Project Wannabe は、小説本文の生成（GEN）、続き生成（CONT）、アイデア生成（IDEA）といったタスクごとに、タイトル・あらすじ・プロット・本文・レーティングなどの情報を独自の構造化フォーマットで組み立てて送信します。本モデルは、この入力形式を正しく理解し、各タスクの意図に沿った応答を返せるよう学習しています。 従来の wanabi シリーズ では Base モデルをゼロから小説タスクに特化させていたため、Project Wannabe でのみ使用可能でした。本モデルでは **Instruct モデルをベースに採用する** アプローチに転換し、ベースモデルが持つ汎用的な対話能力・推論能力を維持したまま、創作能力を付与しています。これにより、**Project Wannabe はもちろん、OpenWebUI や SillyTavern など一般的なチャット UI でも利用可能** になりました。
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View details ↗Wanabi-Novelist-12B-GGUF
kawaimasa基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Wanabi-Novelist-24B-GGUF
kawaimasa基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗trocr-base-handwritten-ru
kazars24基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗driver-alert-detector
kazim5基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ru-longformer-base-4096
kazzand基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ru-longformer-tiny-16384
kazzand基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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