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AI 모델 라이브러리/esatapedico/Qwen3.8-27B-NVFP4-MTP-GGUF
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Qwen3.8-27B-NVFP4-MTP-GGUF

esatapedico · Qwen3.8-27B-NVFP4-MTP-GGUF

A **family of nine GGUF files** of `Qwen3.8-27B` (the native vision-language 27B dense model, Gated DeltaNet + Gated Attention hybrid layout, 262,144-token native context, MTP speculative head), converted from unsloth/Qwen3.8-27B-NVFP4. The **MTP (multi-token prediction) speculative head is baked into every file** — no separate drafter needed. - **`ORIG`** — the source-preserving conversion: native **NVFP4 MLP backbone** + **BF16 attention/embeddings** (the source's F8 attention is dequantized to BF16 because GGML has no F8 tensor type). This is the largest file and the one all tiers are derived from. - **`VERY-LOW` / `COMPACT-LOW` / `LOW` / `MEDIUM` / `MID-HIGH` / `HIGH` / `VERY-HIGH`** — a compact family sharing a **byte-identical 448-tensor NVFP4 backbone** (all attention + MLP re-quantized to NVFP4), differing only in the 10 "extra" tensors (LM head, token embedding, MTP draft head). `COMPACT-LOW` fills the gap between `VERY-LOW` and `LOW` — slightly smaller than `LOW` while keeping a materially stronger LM head than `VERY-LOW` (Q4_K vs Q3_K). `MID-HIGH` sits between `MEDIUM` and `HIGH` with all three head groups at **Q8_0**. - **`HIGHEST`** — the top tier: keeps the source's native **NVFP4 MLP** (layers 0-55) exactly as in `ORIG`, restores **Q8_0** for attention + the late MLP layers + the LM head, and keeps the **token embedding + MTP head in BF16**. The closest compact approximation of the source layout, for high-end GPUs. The goal: **keep native NVFP4 density across the whole model for Blackwell**, and offer a size/precision ladder for the tensors that most affect output quality and decode speed. On our dual 16 GB Blackwell setup every tier fits and runs (see notes before treating any numbers as meaningful).

번역 대기 중입니다. 원문을 표시합니다.

텍스트 생성multimodallicense:apache-2.0
컨텍스트 창미공개
입력 → 출력텍스트 → 텍스트
모델 웨이트미확인
정보 업데이트2026/11/2 19:16:19
OVERVIEW

모델 소개

A **family of nine GGUF files** of `Qwen3.8-27B` (the native vision-language 27B dense model, Gated DeltaNet + Gated Attention hybrid layout, 262,144-token native context, MTP speculative head), converted from unsloth/Qwen3.8-27B-NVFP4. The **MTP (multi-token prediction) speculative head is baked into every file** — no separate drafter needed. - **`ORIG`** — the source-preserving conversion: native **NVFP4 MLP backbone** + **BF16 attention/embeddings** (the source's F8 attention is dequantized to BF16 because GGML has no F8 tensor type). This is the largest file and the one all tiers are derived from. - **`VERY-LOW` / `COMPACT-LOW` / `LOW` / `MEDIUM` / `MID-HIGH` / `HIGH` / `VERY-HIGH`** — a compact family sharing a **byte-identical 448-tensor NVFP4 backbone** (all attention + MLP re-quantized to NVFP4), differing only in the 10 "extra" tensors (LM head, token embedding, MTP draft head). `COMPACT-LOW` fills the gap between `VERY-LOW` and `LOW` — slightly smaller than `LOW` while keeping a materially stronger LM head than `VERY-LOW` (Q4_K vs Q3_K). `MID-HIGH` sits between `MEDIUM` and `HIGH` with all three head groups at **Q8_0**. - **`HIGHEST`** — the top tier: keeps the source's native **NVFP4 MLP** (layers 0-55) exactly as in `ORIG`, restores **Q8_0** for attention + the late MLP layers + the LM head, and keeps the **token embedding + MTP head in BF16**. The closest compact approximation of the source layout, for high-end GPUs. The goal: **keep native NVFP4 density across the whole model for Blackwell**, and offer a size/precision ladder for the tensors that most affect output quality and decode speed. On our dual 16 GB Blackwell setup every tier fits and runs (see notes before treating any numbers as meaningful).

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

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출처 및 추적 기록

huggingface · esatapedico/Qwen3.8-27B-NVFP4-MTP-GGUF ↗2026/10/29 05:24:04
huggingface · enrich:c663b4f2-0202-4076-ad7d-5721fa1c9ff2 ↗2026/10/29 00:30:45

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。