MiniMax-M3.1-Flash-VQ-GGUF

モデル紹介

> [!WARNING] > **Problems or odd output?** Post them in the > Community tab, even as > rough notes. The prompt, sampling settings and runtime commit help most. **MiniMax's 428B native multimodal MoE with its full 1,048,576-token context, its sparse attention, its vision/video tower and its DSpark draft head, sized for one 128 GB DGX Spark.** The 236 GB checkpoint (NVFP4 experts, MXFP8 attention) becomes **115.5 GB** of GGUF parts, of which **111.3 GB (103.7 GiB) stay resident**; the BF16 token table and the vision tower are read from SSD, and the attention K/V of a long session lives on SSD, addressed block by block by the model's own MSA indexer. > **Runtime support, 2026-10-24:** runs on one DGX Spark with the ds4-dfm-rs `minimax_m3_vq` family > (merged PR #110): text, images and video, the DSpark draft head and the native context. > **Performance, 2026-10-29:** matched 2K–64K Prefill **1,001.85 → 1,020.29 tok/s (+1.84%)**; > the last 2K at 64K reaches **990.08 tok/s (+2.87%)**, using the runtime changes in > PR #126 (conditions and Decode limits below). > The historical 1,048,319-token prompt prefilled at 580 tok/s and > decoded. Converted 2026-10-23: 61 independent GGUF parts (115,526,325,376 bytes), every payload > hash in `tensor-manifest.json`, all 89,729 source tensors accounted for; codebooks fitted with > calibration statistics from 4.18M tokens (text, images and video) run through the original > checkpoint. **Measured against the original, teacher-forced on held-out chats: KL 0.150 and the > same next token 88.2% of the time on assistant turns** (the original batched differently: KL > 0.003). The memory and kernel budgets below are the design computed from exact byte layouts.

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。

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。

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。

よくある質問

出典と追跡記録

许可证：other。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。