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Mitsuba_and_HiMitsuba-27B-GGUF

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llama.cpp
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ptq1_0
comfyui
prompt-generation
vision
lora
uncensored
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metadata 
license: apache-2.0
base_model: Qwen/Qwen3.8-27B
pipeline_tag: image-text-to-text
library_name: llama.cpp
language:
  - ja
  - en
tags:
  - gguf
  - ternary
  - pq2_0
  - ptq1_0
  - comfyui
  - prompt-generation
  - vision
  - lora
  - uncensored

Mitsuba & HiMitsuba 27B GGUF

Repository renamed on 2026-10-04 from Mitsuba-ComfyUI-27B-GGUF (old links redirect here). The model files are unchanged. / 2026-10-04 に Mitsuba-ComfyUI-27B-GGUF から改名しました（旧 URL は自動で転送）。モデルのファイルは変わっていません。

A ternary (1.58-bit) Qwen3.8-27B tuned for ComfyUI work: writing image/video generation prompts that follow strict conditions, and describing images. It is not for coding.

ComfyUI 向けに調整した、Qwen3.8-27B の三値（1.58 ビット）モデルです。システムプロンプトにそった画像・動画用プロンプトの作成と、画像の説明が得意です。コーディングには向きません。

  • Self-made ternarization of the official Qwen3.8-27B weights (not derived from Bonsai's weights), stored in Prism ML's PQ2_0 / PTQ1_0 GGUF formats.
  • 7.3 GB (PQ2_0) / 6.0 GB (PTQ1_0). Runs on a single 16 GB GPU.
  • New (2026-10-04): HiMitsuba-Uncensored-LoRA.gguf (70 MB, for PQ2_0) — an optional LoRA that makes the model answer adult/sensitive requests it would otherwise refuse, while leaving ordinary requests unchanged. See the HiMitsuba section below. 追加（2026-10-04）: 秘三葉（HiMitsuba・PQ2_0 用）＝素の Mitsuba が断る大人向け・際どい依頼に答えるようになる LoRA（70MB・任意）。ふだんの依頼の答えは変わりません。

Files

File Size Notes
Mitsuba-ComfyUI-27B/Model-v1.18/Mitsuba-ComfyUI-27B-v1.18-PQ2_0.gguf 7.32 GB Recommended
Mitsuba-ComfyUI-27B/Model-v1.18/Mitsuba-ComfyUI-27B-v1.18-PTQ1_0.gguf 6.00 GB Same weights as PQ2_0, smaller. Vision is lower with the current PTQ1_0 kernel (see below)
Mitsuba-ComfyUI-27B/HiMitsuba-Uncensored-LoRA.gguf 0.07 GB Optional. For PQ2_0 only. Uncensored LoRA for v1.18 PQ2_0 (not for PTQ1_0). Add --lora-scaled HiMitsuba-Uncensored-LoRA.gguf:1.0 (see below)
Mitsuba-ComfyUI-27B/Model-v1.18/mmproj-Q8_0.gguf 0.63 GB Vision encoder. Taken unchanged from OS-Software/Ternary-Bonsai-2-27B-Uncensored-Heretic-GGUF (Apache-2.0)
Mitsuba_bridge_NVIDIA/ (HiMitsuba_start.bat, HiMitsuba_ComfyUI.bat, HiMitsuba_stop.bat, Mitsuba_bridge.py) — Optional. Double-click start on Windows (see Easy start on Windows)
Mitsuba_bridge_AMD/ (HiMitsuba_start_AMD.bat, HiMitsuba_ComfyUI_AMD.bat, HiMitsuba_stop_AMD.bat, HiMitsuba_diag_AMD.bat, Mitsuba_bridge.py) — Optional, beta. The same for AMD Radeon (see AMD Radeon (beta))
docs/ — Documents only, no need to download: EVALUATION.md (detailed evaluation), the charts used on this page (comparison.png, noninferiority.png, two_forms.png, comparison_lora.png / comparison_lora_mobile.png), LICENSE, NOTICE

Evaluation (summary)

Measured with the same questions and conditions for all four models. Details: EVALUATION.md. The last column is the original, un-quantized Qwen3.8-27B (BF16), shown as the reference point: it shows what the ternarization kept and what it gave up.

comparison

All 10 axes (score out of 100 each). Bold = best of the three ternary models. / 10 項目すべての点（各 100 点満点）。太字は三値の 3 本の中で一番。右端は三値化する前の元のモデル（BF16）で、比べる基準として載せています。

Axis / 軸 Mitsuba PQ2_0 Mitsuba PTQ1_0 Ternary Bonsai 2 27B PQ2_0 Qwen3.8-27B BF16 (original / 元)
Total / 総合 61.5 (B) 60.2 (B) 59.6 (B) 66.3 (A)
1. Uncensored / 無検閲度 53.2 48.8 36.0 31.9
2. Honesty / 正直さ 63.2 72.2 51.0 51.1
3. Self-control / 自制心 62.7 56.9 65.5 55.6
4. Directness / 率直さ 84.0 92.0 88.0 84.0
5. Rule following / 正答率 84.0 80.0 72.0 84.0
6. Task completion / 到達率 76.0 80.0 76.0 72.0
7. Coding / コーディング 4.0 4.0 38.0 66.0
8. Reading / 読解力 48.0 40.0 44.0 76.0
9. Japanese & prompts / 文章・プロンプト 52.0 48.0 42.0 52.0
10. Vision / 画像認識 87.8 79.6 83.7 89.8
└ Image/video prompt generation (all conditions met) / 生成プロンプト 6/10 5/10 2/10 3/10
Decode speed (t/s, RTX 5090) / 生成速度 119.0 98.7 120.8 1.6 *

* BF16 (51 GB) does not fit in the 5090's 32 GB, so only 28 of 64 layers ran on the GPU. Its speed is for reference only. * BF16（51GB）は 5090 の 32GB に入りきらず、64 層中 28 層だけを GPU で動かしました。速度は参考値です。

Compared with the original: the ternarization gave up coding (66 → 4) and long-document reading (76 → 48), and kept vision (89.8 → 87.8), rule following (84 → 84) and Japanese & prompts (52 → 52). Prompt generation with all conditions met went up (3/10 → 6/10). 元のモデルと比べると、三値化で手放したのはコーディング（66 → 4）と長文の読解（76 → 48）で、画像認識（89.8 → 87.8）・正答率（84 → 84）・文章とプロンプト（52 → 52）は残しています。条件をすべて満たす生成プロンプトは上がりました（3/10 → 6/10）。

Is Mitsuba not worse than the plain Bonsai? / 素の Bonsai に劣らないか

Paired comparison on the same questions (Mitsuba PQ2_0 minus Ternary Bonsai 2 27B PQ2_0). Directness, task completion and reading were measured with 4× the questions. 同じ問題を対にして比べました（Mitsuba PQ2_0 − 素の Bonsai）。率直さ・到達率・読解力は問題を 4 倍にして測っています。

noninferiority

  • Better (superior) / 優越: uncensored, honesty
  • Not worse (non-inferior, margin 10 points) / 非劣性（許容幅 10 点）: rule following, directness, Japanese & prompts, vision
  • Not decided even with 49–100 questions / 49〜100 問でも判定できず: self-control, task completion, reading
  • Coding is clearly worse and is left out of the chart. / コーディングは明らかに劣るため図から除いています。

Uncensored (無検閲度) = how often the model answers sensitive requests instead of refusing. The plain Mitsuba is not an uncensored model; it still refuses about half of them. If you need those answers, add the HiMitsuba LoRA (next section). 無検閲度＝際どい依頼に断らず答える割合です。素の Mitsuba は無検閲モデルではなく、約半分は断ります。必要な人は次の節の秘三葉 LoRA を足してください。

HiMitsuba — Uncensored LoRA for PQ2_0 / 秘三葉（PQ2_0 用・無検閲 LoRA）

HiMitsuba-Uncensored-LoRA.gguf (70 MB) is an optional LoRA adapter for Mitsuba v1.18 PQ2_0. "Hi" (秘) means "hidden / private" in Japanese.

What it does

  • The LoRA switches by itself. For ordinary requests (SD/Krea/video prompts, describing images) it answers exactly like the plain Mitsuba. For adult or sensitive requests, which the plain Mitsuba refuses or waters down, it answers.
  • Censorship on adult topics comes in two forms, and HiMitsuba reduces both. (1) Refusal: the model declines. (2) Evasion: the model appears to answer but quietly drops what was asked (vague wording, skipped parts, a lecture instead of the content). The plain Mitsuba does both; many "uncensored" models fix only the first. HiMitsuba was tuned on both: refusals 51 → 7 of 106 sensitive requests, and on the sex-explanation questions it did answer, evasive answers fell from 73% to 32%.
  • Nothing else to run: no second "judge" model, no router, no system prompt. One extra flag at start-up.
  • Works with images too: an adult image → a usable generation prompt.
  • It is not an "answer anything" model. It still often refuses requests for help with crimes, weapons or harming others (see the table).

two forms of censorship

How this differs from a "judge" approach. Another way to build an uncensored switch is a separate small judge model, a Jev-style model that only answers yes/no (for example Jeff-Qwen3.5-0.8B): it looks at each request, and the caller then decides which LoRA or model to use. That needs a second model loaded and code on the caller's side. HiMitsuba has no judge: the decision is inside the LoRA's weights, so the same model simply answers differently. The trade-off is that it is always on and cannot be tuned per request.

判定役を置く方式との違い: 小さな判定専用モデル（yes/no だけ答える Jev 型。例 Jeff-Qwen3.5-0.8B）に依頼を見せ、呼び出す側が LoRA やモデルを切り替えるやり方があります。モデルが 2 本要り、呼ぶ側にコードも要ります。秘三葉には判定役がなく、判断は LoRA の重みの中に溶けていて、同じモデルの答え方が自分で変わります。代わりに常に掛かっていて、依頼ごとの調整はできません。

What it is not

  • It is not a new model. The 7.3 GB base is unchanged; the LoRA is applied at load time.
  • It is made for v1.18 PQ2_0 only. It was not tested on PTQ1_0 or on other Qwen3.8-27B GGUFs, and it will not load on models with a different architecture.

秘三葉は Mitsuba v1.18 PQ2_0 用の LoRA（70MB・任意・PTQ1_0 には使えません）です。アダルトの検閲には「拒否」と「回避」の2種類があり、秘三葉はその両方を減らします。拒否＝断る。回避＝答えたふりをして肝心な所を抜く（ぼかす・省く・説教に替える）。素の Mitsuba は両方をやり、世の「無検閲」モデルの多くは拒否しか直していません。秘三葉は両方を狙って調整し、拒否は 106 問中 51 → 7、答えた性の説明問題の中の回避は 73% → 32% になりました。LoRA が自分で切り替えます＝ふだんの依頼（画像・動画のプロンプト作成、画像の説明）は素の Mitsuba と同じ答え、大人向け・際どい依頼には断らずに答えます。判定役のモデルもルーターも設定文も要りません。起動の引数を 1 つ足すだけです。「何でも答える」モデルではなく、犯罪・武器・他人を害する手助けはいまも断ることが多いです。

How to use / 使い方

  1. Download HiMitsuba-Uncensored-LoRA.gguf and put it in the same folder as the model (Mitsuba-ComfyUI-27B-v1.18-PQ2_0.gguf).
  2. Add one flag to the llama-server command: --lora-scaled HiMitsuba-Uncensored-LoRA.gguf:1.0
llama-server -m Mitsuba-ComfyUI-27B-v1.18-PQ2_0.gguf --mmproj mmproj-Q8_0.gguf --no-mmproj-offload --reasoning off ^
  --lora-scaled HiMitsuba-Uncensored-LoRA.gguf:1.0 ^
  --jinja --temperature 0.6 --top-k 20 --top-p 0.95 ^
  --ctx-size 131072 --cache-type-k q4_0 --cache-type-v q4_0 --n-gpu-layers 99
  • Start llama-server from that folder, or give the LoRA path relative to it. On Windows, a full path with a drive letter (D:\...gguf:1.0) breaks the file:scale syntax because of the colon.
  • :1.0 is the strength. 1.0 is what was evaluated. 0.0 = off.
  • ComfyUI (Ollama-compatible nodes, OpenCode, etc.) need no change: the LoRA is applied on the server side.
  • On Windows, the double-click files in Easy start on Windows below do all of this for you. / Windows なら、下の「かんたん起動」の bat がこれを全部やります。

使い方: ① LoRA を本体と同じフォルダに置く ② 起動コマンドに --lora-scaled HiMitsuba-Uncensored-LoRA.gguf:1.0 を 1 行足す。それだけです。 Windows でフルパス（D:\...gguf:1.0）を書くとドライブの「:」が区切りと衝突して失敗するので、本体のフォルダから起動してファイル名だけを書いてください。:1.0 は強さで、評価は 1.0 で取りました。ComfyUI 側の設定は変えません（LoRA はサーバー側で効きます）。

Evaluation / 評価（素の Mitsuba と同じ 10 項目・同じ条件）

comparison_lora

Same 10-axis evaluation as above, plain Mitsuba v1.18 PQ2_0 vs. the same model with HiMitsuba at 1.0. Full domain breakdown: EVALUATION.md.

Axis / 軸 Mitsuba v1.18 + HiMitsuba
Total / 総合 61.5 (B) 66.9 (A)
1. Uncensored / 無検閲度 53.2 89.6
  └ Refused / 拒否（106 問中） 51 (48%) 7 (7%)
  └ Evasive, of the sex-explanation questions it answered / 回避率（答えた性の説明問題の中） 8/11 (73%) 6/19 (32%)
  └ Refused or evasive / 拒否＋回避（106 問中） 59 (56%) 13 (12%)
2. Honesty / 正直さ 63.2 73.3
3. Self-control / 自制心 62.7 48.2
4. Directness / 率直さ 84.0 92.0
5. Rule following / 正答率 84.0 84.0
6. Task completion / 到達率 76.0 92.0
7. Coding / コーディング 4.0 4.0
8. Reading / 読解力 48.0 44.0
9. Japanese & prompts / 文章・プロンプト 52.0 52.0
  └ Image/video prompt generation / 生成プロンプト 6/10 8/10
10. Vision / 画像認識 87.8 89.8
Decode speed (t/s, RTX 5090) / 生成速度 119.0 103.1
VRAM, CTX 131K / 262K (KV q4_0) / 必要 VRAM 9.5 / 12.4 GB 9.5 / 12.4 GB
  • Uncensored, both kinds: refusals fell from 51 to 7 of 106 sensitive requests, and evasive answers (answered, but with the asked-for content dropped) fell from 73% to 32% of the answered sex-explanation questions. Refused-or-evasive together: 59 → 13 of 106. The remaining refusals are mostly "help with crimes / weapons" and "help with abuse" (both about 65%).
  • ComfyUI work did not get worse: rule following, Japanese and vision are the same or higher, and prompt generation with all conditions met went 6/10 → 8/10.
  • What got worse: self-control (62.7 → 48.2; in the "error hell" test it stops retrying after the first tool error) and speed (119 → 103 t/s, the cost of applying a LoRA at run time). VRAM is unchanged.
  • The two uncensored Bonsai variants in the chart (CRACK, heretic) are other people's models, measured here only for comparison.

無検閲度は 53.2 → 89.6。拒否は 51 → 7／106 問、回避（答えたのに肝心な所を抜く）は答えた性の説明問題の 73% → 32%、拒否＋回避を合わせると 59 → 13／106 問。残る拒否は「犯罪・武器」「悪用の手助け」が中心です。ComfyUI の仕事（正答率・文章・画像）は下がらず、条件を全部守る生成プロンプトは 6/10 → 8/10 に上がりました。下がったのは自制心（62.7 → 48.2。道具がエラーを返すと 1 回で諦めます）と速度（119 → 103 t/s。LoRA を実行時に掛ける分）です。VRAM は変わりません。

Easy start on Windows (double-click) / かんたん起動（Windows・ダブルクリック）

Download the folders as they are on this page (the .bat files find the model in ../Mitsuba-ComfyUI-27B/Model-v1.18/), or put all the files below in one folder. Then double-click a .bat in Mitsuba_bridge_NVIDIA/ (NVIDIA) or Mitsuba_bridge_AMD/ (AMD Radeon, beta). Nothing is installed into ComfyUI or into Windows.

File / ファイル What it is / 中身
Mitsuba-ComfyUI-27B/Model-v1.18/Mitsuba-ComfyUI-27B-v1.18-PQ2_0.gguf (or PTQ1_0) the model / 本体
Mitsuba-ComfyUI-27B/Model-v1.18/mmproj-Q8_0.gguf image input / 画像の入力
Mitsuba-ComfyUI-27B/HiMitsuba-Uncensored-LoRA.gguf optional: HiMitsuba / 任意（秘三葉）
Mitsuba_bridge_NVIDIA/HiMitsuba_start.bat for AI apps (OpenAI-compatible) / AI アプリ用
Mitsuba_bridge_NVIDIA/HiMitsuba_ComfyUI.bat for ComfyUI's Ollama nodes / ComfyUI の Ollama ノード用
Mitsuba_bridge_NVIDIA/HiMitsuba_stop.bat stop and give all VRAM back / 止めて VRAM を全部返す
Mitsuba_bridge_NVIDIA/Mitsuba_bridge.py used by HiMitsuba_ComfyUI.bat / ComfyUI 用 bat が使う

AI apps (OpenCode, Open WebUI, SillyTavern, Continue, ...) — double-click HiMitsuba_start.bat, then set in the app:

  • Base URL: http://127.0.0.1:8080/v1 (OpenAI-compatible)
  • Model: himitsuba (with the LoRA) or mitsuba (plain)
  • API key: anything (e.g. none)

ComfyUI — you need an Ollama node already (e.g. comfyui-ollama). Double-click HiMitsuba_ComfyUI.bat, then in Ollama Connectivity:

  • url: http://127.0.0.1:11434 — if a real Ollama already uses 11434, the window prints the port it used instead
  • model: himitsuba or mitsuba
  • keep_alive: 0 (recommended) = the VRAM is given back right after the answer, before the image model loads. The default 5 = given back after 5 idle minutes.

Stop: double-click HiMitsuba_stop.bat, or close the window.

Notes:

  • First run downloads the PrismML build of llama.cpp (prism-b10685, about 540 MB) into bin\, and for ComfyUI a private Python 3.12 (about 11 MB, from python.org) into python\. Later starts take a few seconds.
  • NVIDIA GPU, Windows 10/11. About 10 GB of VRAM while a model is loaded, 0 while idle. Picking the other model unloads the current one.
  • Folder names with Japanese or spaces are fine (a plain-name junction is made under C:\ProgramData\HiMitsuba\; nothing is copied).
  • Thinking is already turned off in this setup.
  • Mitsuba_bridge.py is derived from ComfyUI-Bonsai-Bridge by the same author.

このページのフォルダの形のまま落とす（bat が ../Mitsuba-ComfyUI-27B/Model-v1.18/ の本体を見つけます）か、上のファイルを全部1つのフォルダに置いて、Mitsuba_bridge_NVIDIA/（NVIDIA）か Mitsuba_bridge_AMD/（AMD Radeon・β版）の bat をダブルクリックするだけです。ComfyUI にも Windows にも何もインストールしません。

  • AI アプリ（OpenCode・Open WebUI・SillyTavern など）: HiMitsuba_start.bat を押し、アプリの接続先を http://127.0.0.1:8080/v1（OpenAI 互換）、モデルを himitsuba（秘三葉）か mitsuba（素）、API キーは何でも、にします。
  • ComfyUI: Ollama のノード（comfyui-ollama など）が入っていることが前提です。HiMitsuba_ComfyUI.bat を押し、Ollama Connectivity の url を http://127.0.0.1:11434、model を himitsuba にします。keep_alive は 0 がおすすめです（答えた直後に VRAM を返すので、画像モデルと取り合いません）。既定の 5 のままなら、使わずに 5 分たつと返します。
  • 止める: HiMitsuba_stop.bat を押すか、窓を閉じます。
  • 初回だけ、PrismML 版の llama.cpp（約 540 MB）と、ComfyUI 用の小さな Python（約 11 MB）を自動で落とします。2 回目からは数秒で起動します。
  • NVIDIA の GPU と Windows 10/11 が必要です。モデルを載せている間は約 10 GB の VRAM を使い、使っていない時は 0 です。
  • 日本語や空白の入ったフォルダでも動きます。

AMD Radeon (beta) / AMD Radeon（β版）

The _AMD.bat files do the same as the NVIDIA ones, but download the PrismML HIP (Radeon) build of the same llama.cpp release (prism-b10685, about 330 MB) into bin-hip\. PrismML lists PQ2_0 as supported on HIP, and the HIP build includes RDNA4 (RX 9070 / 9060: gfx1200 / gfx1201), RDNA3 and RDNA2 (gfx1030). Not yet tested by the author on real AMD hardware.

File Use
Mitsuba_bridge_AMD/HiMitsuba_start_AMD.bat for AI apps (OpenAI-compatible), same URL and model names as above
Mitsuba_bridge_AMD/HiMitsuba_ComfyUI_AMD.bat for ComfyUI's Ollama nodes
Mitsuba_bridge_AMD/HiMitsuba_stop_AMD.bat stop and give all VRAM back
Mitsuba_bridge_AMD/HiMitsuba_diag_AMD.bat if it does not work: writes HiMitsuba_diag_report.txt (Windows, GPU, driver, VRAM, whether llama.cpp sees the GPU, and an optional 1–2 minute load test). Your user name and PC name are hidden. Please paste it in the Community tab
  • Keep the AMD Adrenalin driver up to date (the HIP runtime comes with the driver).
  • Use the PQ2_0 file. Whether PTQ1_0 runs on HIP is not confirmed.
  • About 10 GB of VRAM while loaded (a 16 GB card is enough).

_AMD.bat は NVIDIA 用と同じ動きで、同じ版の PrismML llama.cpp の HIP（Radeon）版（約 330 MB）を bin-hip\ に落とします。PrismML は PQ2_0 を HIP 対応としており、HIP 版には RDNA4（RX 9070 / 9060）・RDNA3・RDNA2 が入っています。作者はまだ AMD の実機で試せていません。 うまく動かない時は HiMitsuba_diag_AMD.bat を押すと HiMitsuba_diag_report.txt（Windows・GPU・ドライバー・VRAM・GPU を認識できているか・任意の 1〜2 分の読み込み試験。ユーザー名と PC 名は伏せます）ができるので、Community タブに貼ってください。ドライバー（AMD Adrenalin）は新しいものにしてください。本体は PQ2_0 を使ってください。

How to run

The model and mmproj-Q8_0.gguf are in Mitsuba-ComfyUI-27B/Model-v1.18/, the LoRA in Mitsuba-ComfyUI-27B/. / 本体と mmproj は Mitsuba-ComfyUI-27B/Model-v1.18/、LoRA は Mitsuba-ComfyUI-27B/ にあります。

PQ2_0 and PTQ1_0 need the PrismML fork of llama.cpp (upstream llama.cpp does not support these formats yet): https://github.com/PrismML-Eng/llama.cpp (branch prism).

The settings used for the evaluation:

llama-server -m Mitsuba-ComfyUI-27B-v1.18-PQ2_0.gguf --mmproj mmproj-Q8_0.gguf --no-mmproj-offload --reasoning off ^
  --jinja --temperature 0.6 --top-k 20 --top-p 0.95 ^
  --ctx-size 131072 --cache-type-k q4_0 --cache-type-v q4_0 --n-gpu-layers 99

Thinking was off ("chat_template_kwargs": {"enable_thinking": false}). In every test, the conditions (word count, required and forbidden words, output format) were written in the request text itself.

Turn thinking OFF / 思考は必ずオフで

Use this model with thinking (reasoning) turned OFF. It was tuned only in no-thinking mode. With thinking on, it tends to repeat the same sentence in its reasoning and can end without writing an answer.

  • llama-server: add --reasoning off (or set reasoning = off in a models preset)
  • Per request: "chat_template_kwargs": {"enable_thinking": false}
  • OpenCode and other agents: set the model to "reasoning": false

このモデルは思考（reasoning）をオフにして使ってください。 思考オフの形だけで調整しています。思考をオンにすると、思考の中で同じ文を繰り返し、答えを書かないまま終わることがあります。 llama-server なら --reasoning off、リクエストごとなら "chat_template_kwargs": {"enable_thinking": false} を指定します。

Measured: the same evaluation with thinking ON. / 思考オンで同じ評価をした結果:

Axis / 軸 PQ2_0 OFF PQ2_0 ON PTQ1_0 OFF PTQ1_0 ON
Total / 総合 61.5 53.2 60.2 54.5
Vision / 画像認識 88 55 80 63
Rule following / 正答率 84 60 80 64
Uncensored / 無検閲度 53 15 49 24
Image/video prompt generation / 生成プロンプト 6/10 6/10 5/10 5/10
Reading / 読解力 48 68 40 68

With thinking ON, many answers came back empty: the model finished its reasoning and stopped without writing the answer (vision: 19 of 49 on PQ2_0, 14 of 49 on PTQ1_0). Only long-document reading improved. 思考オンでは、考えたあと答えを書かずに終わる「空の答え」が多く出ました（画像 49 問中、PQ2_0 で 19 問・PTQ1_0 で 14 問）。上がったのは長文の読解だけです。

What it is good at

  • Stable Diffusion style prompts: English tags within a given count, required words included, a final Negative: line, and forbidden words kept out.
  • Video prompts in time segments (0-3s: / 3-6s: / 6-9s:) with a camera move in each segment.
  • Describing images: objects, counts, text in images, charts, scenes, people, and comparing several images.

Limitations

  • Coding: do not use. It scores 4/100 on our coding test (Bonsai: 38).
  • Long-document reading is average (48).
  • The plain model is not uncensored; it refuses some sensitive requests. Use the HiMitsuba LoRA if you need those answers (it still refuses help with crimes and harming others).
  • With HiMitsuba: self-control drops (stops retrying after a tool error) and decode speed is about 13% lower.
  • Prompt generation passes about 6 of 10 strict test cases. Check the output against your conditions.

License and attribution

  • This model is released under the Apache License 2.0 (LICENSE). It is a modified version of Qwen3.8-27B.
  • See NOTICE for attributions.

日本語の補足

  • 推奨は PQ2_0 です。PTQ1_0 は重みは同じですが、今の llama.cpp の PTQ1_0 用の計算では画像の点が下がります。
  • 評価の詳しい表と、その見方は EVALUATION.md にあります。
  • 秘三葉（HiMitsuba）LoRA は任意です。入れなければ素の Mitsuba のままです。入れても ComfyUI の仕事の点は下がりません（上の表）。