/ Модель

Характеристики

Лицензия
Apache 2.0
Базовая модель
Underdog-Saluki-27B-1.0
Библиотека
gguf
Опубликована
8 окт 2026

Underdog-Saluki-27B-1.0-MTP-GGUF

Генерация текстаТекст → текст

Русское описание этой модели ещё готовится. Пока можно заглянуть в карточку модели от автора ниже.

14 269скачиваний за 30 дней26лайковвеса обновлены8 окт 2026

Бенчмарки

Как модель справилась с задачами пользователей ailist и что о ней публикуют открытые лидерборды

Публичные оценки

Из открытых лидербордов. Сравнивайте результаты одного теста при одинаковых условиях — отсутствие оценки не значит, что модель хуже.

В подключённых источниках результатов для этой модели пока нет.

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

Underdog Saluki 27B 1.0 + MTP (GGUF)

Underdog Saluki 27B 1.0 with the Qwen3.8-27B multi-token-prediction (MTP) head added, so llama.cpp can run it with self-speculative decoding (--spec-type draft-mtp). The original Saluki release has no MTP head.

FileUnderdog-Saluki-27B-1.0-IQ2-mix-MTP.gguf, 8.35 GB
sha25698f6ebb527917a2541df8e667d51c3ec8a8bb135b44c606d40cc6ed924e89e52
Main weightsall 851 tensors byte-identical to Saluki's Underdog-Saluki-27B-1.0-IQ2-mix.gguf (sha256 4a673518…5d9efb, revision 1336c0b)
Added15 tensors blk.64.* (the MTP layer, Q8_0/F32, ~0.45 GB); qwen35.block_count 64 → 65; qwen35.nextn_predict_layers = 1
MTP head originQwen3.8-27B's own MTP head as quantized in unsloth/Qwen3.8-27B-GGUF, taken verbatim from BoldingBuilds/Ternary-Bonsai-2-27B-Abliterated-v2-PQ2_0-MTP-GGUF (sha256 a4e4c7b5…3ebebf8)

Nothing was trained or re-quantized. Saluki is built from Qwen3.8-27B, so the head is the one that belongs to its base model.

Usage

Works with stock llama.cpp (tested on upstream 03aa006, 2026-10-08, just after release b11493) and any build that has MTP drafting (--spec-type draft-mtp).

llama-server -m Underdog-Saluki-27B-1.0-IQ2-mix-MTP.gguf --jinja -ngl 99 -fa on \
  -c 32768 -np 1 -ctk q8_0 -ctv q8_0 \
  --spec-type draft-mtp --spec-draft-n-max 2

Sampling and thinking settings are the same as for Saluki (see its card). Without the --spec-type flags the file runs like the original Saluki: llama.cpp logs the MTP tensors as unused, skips them, and uses the same VRAM as the original file.

Measured (one machine)

RTX 3080 12 GB, WSL2, 32K context, q8_0 KV cache, -np 1, 6 prompts (code / Japanese / English, thinking on and off), temperature 0.

Stock llama.cpp (upstream 03aa006):

Original SalukiThis file, no --spec-typeThis file, MTP on
Generation (mean of 6)41.2 tok/s40.7 tok/s66.2 tok/s (58–75)
Draft acceptance——62–96%
VRAM10.3 GB10.3 GB11.5 GB

PrismML fork prism-b10754-2459f68 (PrismML-Eng/llama.cpp): 42.7 → 68.2 tok/s (58–77), acceptance 60–96%, 10.9 GB VRAM with MTP on.

  • Not bit-identical to Saluki at temperature 0. The MTP path verifies drafts in batches, which changes floating-point accumulation order, so near-tied tokens sometimes flip (e.g. "約2万円" vs "2万円"). In 6 test prompts, 1 matched exactly and 5 diverged at a near-tie, after 9–95% of the text.
  • 12 GB cards: keep the context at 32K. With 64K context + q8_0 KV + MTP the VRAM overflowed on this machine and speed fell to 24–29 tok/s.
  • No new benchmarks: the quality numbers on Saluki's card are for the original file. Two small agent tasks (write a module with tests; fix three bugs) were passed through DeepSeek Harness with MTP on.

Reproduce

graft_mtp.py in this repo builds the file from the two upstream GGUFs (needs gguf-py from llama.cpp):

ALLOW_TYPE_MISMATCH=1 python graft_mtp.py <llama.cpp>/gguf-py \
  Underdog-Saluki-27B-1.0-IQ2-mix.gguf \
  Ternary-Bonsai-2-27B-Abliterated-v2-PQ2_0-MTP.gguf \
  Underdog-Saluki-27B-1.0-IQ2-mix-MTP.gguf

Only the donor's blk.64.* tensors and MTP metadata are used. ALLOW_TYPE_MISMATCH=1 is needed because the donor's main weights are a different quantization (PQ2_0); they are checked for matching shapes and then ignored.

Credits and license

Apache-2.0, see LICENSE and NOTICE.

  • Underdog Saluki 27B 1.0 by Underdog (ConwayResearch), built on Qwen3.8-27B and ISTA-DASLab's Qwen3.8-27B-GSQ-RCO-GGUF.
  • Qwen3.8-27B and its MTP head by the Qwen team.
  • MTP head GGUF quantization by Unsloth (unsloth/Qwen3.8-27B-GGUF).
  • The MTP grafting approach credited by BoldingBuilds to decent-jawfish, ProCreations and sudoingX/qwen38-mtp.

Not affiliated with or endorsed by Underdog/ConwayResearch, Qwen, Unsloth, PrismML or BoldingBuilds.

日本語メモ

Underdog Saluki 27B 1.0 に、Qwen3.8-27B 公式の MTP ヘッド(15 テンソル)を足しただけのファイルです。 本体の重みは元ファイルとバイト単位で同一です。普通の llama.cpp で動きます。RTX 3080 12GB で 41 → 66 tok/s(平均)になりました。 12GB のカードでは文脈を 32K に抑えてください(64K だと VRAM が溢れて遅くなります)。

Как запустить

На своей GPU в ailist. Цена — по текущим предложениям провайдеров

GPU в ailist

llama.cpp

Нельзя запустить

GGUF-модель ждёт проверки: нужно выбрать файл кванта под доступные GPU.

Ревизия весов
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