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Характеристики

Контекст
262 144 токена
Размер весов
31,2 ГБ
Лицензия
Apache 2.0
Архитектура
qwen3_5 · dense
Базовая модель
Qwen3.8-27B-Uncensored-GGUF
Библиотека
mlx
Опубликована
16 авг 2026

Qwen3.8-27B-Uncensored-OrcaRouter-MLX-8bit

Изображение и текст → текст6 профессий

Qwen3.8-27B-Uncensored-OrcaRouter-MLX-8bit — неофициальный нативный MLX-чекпойнт, опубликованный пользователем chimingw и созданный на основе Qwen3.8-27B от Qwen/Alibaba через F16-версию orcarouter/Qwen3.8-27B-Uncensored-GGUF.

3 538скачиваний за 30 дней14лайковвеса обновлены17 авг 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.

Что умеет модель

Описание составлено по карточке модели и метаданным Hugging Face

Это плотная vision-language модель с поддержкой текста и изображений, а также MTP-головой для speculative decoding. Модель предназначена для задач понимания и генерации текста по текстовым и визуальным входам, исследовательских экспериментов, интерпретируемости, red-teaming и оценки устойчивости моделей. Заявленные языки — английский и китайский.

Модель содержит около 27 миллиардов параметров и использует смешанное хранение: подходящие матрицы квантизированы в нативный MLX affine 8-bit с group size 64, а часть тензоров сохраняется в форматах F16, BF16 или F32. Конвертация не включает дополнительное обучение, дообучение, слияние или изменение выравнивания. Формат MLX ориентирован на запуск в экосистеме Apple Silicon. Размер контекстного окна и точные требования к памяти в README не указаны.

К сильным сторонам относятся мультимодальность, поддержка MLX, сохранение исходной точности для ряда тензоров и уменьшенный по сравнению с полной точностью объём хранения. При этом модель является uncensored-версией: согласно заявлению издателя, её защитное выравнивание существенно удалено, поэтому она может отвечать на вредоносные, незаконные и иные опасные запросы. Заявления о поведении, качестве и безопасности не были независимо подтверждены; модель не предназначена для прямого использования конечными пользователями без собственных механизмов модерации и защиты. Лицензия — Apache 2.0.

Для каких профессий подходит6

Разделы каталога профессий, в задачах которых используется эта модель

Все профессии

Также подходит для

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

Qwen3.8-27B-Uncensored-OrcaRouter — MLX 8-bit

GGUF QUANTS HERE: chimingw/Qwen3.8-27B-Uncensored-OrcaRouter-GGUF

This unofficial native MLX affine 8-bit/group-64 checkpoint was independently generated directly from the pinned floating F16 parent orcarouter/Qwen3.8-27B-Uncensored-GGUF@402c3e0a64d77880f55ab096c5b7597ef85162ab. It was not transcoded from another quant and does not use the older FP8-expanded BF16 working representation. This conversion adds no training, fine-tuning, merging, or alignment change.

Source publisher notice — OrcaRouter says

OrcaRouter claims:

An abliterated (refusal-removed) build of Qwen/Qwen3.8-27B, a dense native vision-language model with an MTP speculative-decoding head.

⚠️ Disclaimer — read before use

This model has had its safety alignment substantially removed via abliteration (orthogonalizing the refusal direction out of the residual stream). As a direct consequence:

  • It will comply with harmful, unethical, offensive, or illegal requests that the original Qwen3.8-27B would refuse. It has no meaningful built-in guardrails.
  • It is released strictly for legitimate research — interpretability, AI-safety and refusal-mechanism study, red-teaming, robustness evaluation, and controlled experiments.
  • You assume full responsibility and liability for how you use it and for everything it generates. Do not deploy it to end users or in production without adding your own safety, moderation, and abuse-prevention layers.
  • Use must comply with the Apache 2.0 License inherited from the base model, and all laws and regulations that apply to you.
  • The authors and uploaders accept no liability for any misuse or harm arising from this model. Its outputs do not reflect the views of the uploaders or of Qwen / Alibaba.

By downloading or using this model you acknowledge and accept the above.

These are the source publisher's claims and warnings. This conversion has not independently revalidated refusal-removal, quality, safety, benchmark, or behavioral claims.

Direct floating source and precision audit

Source: orcarouter/Qwen3.8-27B-Uncensored-GGUF@402c3e0a64d77880f55ab096c5b7597ef85162ab, two F16 GGUF shards plus mmproj-Qwen3.8-27B-Uncensored-f16.gguf.

Sampled text and formerly quantized MTP matrices did not lie on the old FP8×scale lattice, establishing higher-precision pre-FP8 values in those samples. A dense mtp.fc.weight sample was exactly the old BF16 values stored as F16. A sampled vision-projector BF16 matrix was byte-identical to the old source-derived BF16 matrix despite the new projector's f16 filename. The projector's two distinct GGUF patch-embedding tensors are ordered temporal slices; both are stacked before the native MLX channels-last transpose, reconciling 334 physical mmproj tensors to 333 native vision tensors without dropping either slice. These audits establish precision lineage, not an intelligence gain.

Model tree

Qwen/Qwen3.8-27B
└── orcarouter/Qwen3.8-27B-Uncensored-GGUF@402c3e0a64d77880f55ab096c5b7597ef85162ab (F16 parent)
    └── chimingw/Qwen3.8-27B-Uncensored-OrcaRouter-MLX-8bit

GGUF is the upstream container of the newly released floating weights; this child is a native MLX package reconstructed from those floating tensors.

MLX storage avoids an unnecessary pre-quantization loss: F16 tensors remain F16 (including the two unquantized token-embedding and MTP-fc tensors), BF16 tensors retain their raw BF16 bytes, and eligible F16/BF16 matrices are quantized directly in that source dtype. A source F32 dense tensor is stored as BF16 only when a full-value BF16-cast→F32 bit comparison reproduces every finite value exactly; otherwise it remains F32. The expected promoted tensors, including both temporal patch slices, are old BF16-lattice values and therefore round-trip losslessly. The recursively validated package contains 1199 logical tensors: 504 eligible matrices encoded as native MLX affine 8-bit/group-64 weights, and 695 dense tensors retained in their source-derived F16/BF16/F32 contract. The package contains 2207 physical tensors totaling 31,194,688,000 tensor-payload bytes. must be replaced with the final recursively validated counts. The required M4 Pro gates exercise this mixed F16/BF16/F32 package.

Integrity and release gates

Pinned source files

FileBytesSHA-256
Qwen3.8-27B-Uncensored-F16-00001-of-00002.gguf27,908,108,288578926d4e6d94281e95a48d8e154c4667061a669e9016f2aa2b15101ab4363dc
Qwen3.8-27B-Uncensored-F16-00002-of-00002.gguf26,749,625,920c15e78454caec46b19dae22fa6915a9e77b917e7cceffdc64c265801f4eaa1f1
mmproj-Qwen3.8-27B-Uncensored-f16.gguf931,145,984add205b7bfdb3f71f6da36b0a82aa20928dd829a920878c602628cdfbebc5288

Published model shards

FileBytesSHA-256
model-00001-of-00007.safetensors4,831,439,05695b2538f7521ec1912a6ae7e33748f3d4dd615daa3e8270ed95ed0dec3b63498
model-00002-of-00007.safetensors4,852,372,272d6f2feec87c865e400fc5abb60d4cb54ea803e0376661cb3bff2653aa3b2ddbb
model-00003-of-00007.safetensors4,840,627,800818a342a50927634d8433fbfd78944acb86c50d5c475e5918de2e955863c092d
model-00004-of-00007.safetensors4,835,657,096f12e8c23e9a0cbde62b979f7d6d253d292cf4dad100fef8682b96be78914832c
model-00005-of-00007.safetensors4,880,831,89698ea59f0a17fada1177c5d38c446ca83d474be68b77d585653a99d849bcd7eae
model-00006-of-00007.safetensors4,840,627,360d315b7aab32c87095a36ec98357c8649386add5511fb2e0fd8e6a7c6e3162176
model-00007-of-00007.safetensors2,113,414,640dc5323be49315c5d471153e4ef6990a7364a35f284b67263c46de444112e954a

Artifact manifest: /workspace/orcarouter-f16/work/release/MLX-8bit/ARTIFACT-MANIFEST.json — e0be3703ec68d3d5776a76b2e32fc7a17f20c76fdc687c2186aff997f394c8fc.

M4 Pro runtime

Validated sequentially on Mac M4 Pro with 48 GiB unified memory, macOS 26.6.1, runtime mlx-serve-26.8.7|mlx-0.32.0|mlx-c-fba4470b8907|llama.cpp-b10034. Text determinism, chat-template tool calling, image-grounded vision (red), and clean unload all passed. MTP-off produced zero draft tokens in 13.868492s; MTP-on drafted 66 tokens and accepted 66 in 6.695466s. The matched output SHA-256 was 65966537023045093dda6a4bf49057afef35319d2f5170c68435d3330c8cec10 in both modes. Durations are bounded smoke-test evidence, not performance benchmarks.

Bounded matched fidelity

ModelPerplexityTokens
old-fp8-derived-Q4_K_M5.80342048
new-f16-derived-Q4_K_M5.79622048
old-fp8-derived-Q5_K_M5.79012048
new-f16-derived-Q5_K_M5.77742048
old-fp8-derived-Q6_K5.85862048
new-f16-derived-Q6_K5.80342048
old-fp8-derived-Q8_05.79582048
new-f16-derived-Q8_05.81952048

Fidelity report: /workspace/orcarouter-f16/work/fidelity-v2/FIDELITY-REPORT.json — 9774c9205abac6feb2ff8759627dc5b2087e703d7141f42a658432a92de02e0d.

Machine-readable release evidence

The JSON below is the fail-closed release contract. It intentionally excludes README/self-manifest hashes to avoid circular evidence.

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    "model-00002-of-00007.safetensors": {
      "sha256": "d6f2feec87c865e400fc5abb60d4cb54ea803e0376661cb3bff2653aa3b2ddbb",
      "size": 4852372272
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    "model-00003-of-00007.safetensors": {
      "sha256": "818a342a50927634d8433fbfd78944acb86c50d5c475e5918de2e955863c092d",
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      "size": 4835657096
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    "model-00005-of-00007.safetensors": {
      "sha256": "98ea59f0a17fada1177c5d38c446ca83d474be68b77d585653a99d849bcd7eae",
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    "model-00006-of-00007.safetensors": {
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    "model-00007-of-00007.safetensors": {
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  },
  "fidelity": {
    "corpus": {
      "path": "wikitext-2-raw/wiki.test.raw",
      "sha256": "173c87a53759e0201f33e0ccf978e510c2042d7f2cb78229d9a50d79b9e7dd08",
      "size": 1290590
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    "report": {
      "path": "/workspace/orcarouter-f16/work/fidelity-v2/FIDELITY-REPORT.json",
      "sha256": "9774c9205abac6feb2ff8759627dc5b2087e703d7141f42a658432a92de02e0d",
      "size": 17333
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    "results": [
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        "label": "old-fp8-derived-Q4_K_M",
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      {
        "label": "new-f16-derived-Q4_K_M",
        "perplexity": 5.7962,
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      {
        "label": "old-fp8-derived-Q5_K_M",
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      },
      {
        "label": "new-f16-derived-Q5_K_M",
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      {
        "label": "old-fp8-derived-Q6_K",
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      },
      {
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        "perplexity": 5.8034,
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      {
        "label": "old-fp8-derived-Q8_0",
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        "token_count": 2048
      },
      {
        "label": "new-f16-derived-Q8_0",
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      }
    ]
  },
  "kind": "mlx",
  "mlx": {
    "bits": 8,
    "bits_passed": [
      4,
      5,
      6,
      8
    ],
    "conversion_report": {
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      "size": 1024
    },
    "dequantize_passed": true,
    "group_size": 64,
    "m4_runtime_passed": true,
    "mode": "affine",
    "quantize_passed": true
  },
  "runtime": {
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        "passed": true,
        "run_1_output_sha256": "2f2d059139883d9985ea4178113bf6ac4fa731a1b72ab0be2608a3a279411144",
        "run_2_output_sha256": "2f2d059139883d9985ea4178113bf6ac4fa731a1b72ab0be2608a3a279411144"
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      "mtp_off": {
        "draft_tokens": 0,
        "mtp_weights_loaded": true,
        "passed": true,
        "speculative_decoding_enabled": false
      },
      "mtp_on": {
        "draft_tokens": 66,
        "mtp_weights_loaded": true,
        "passed": true,
        "speculative_decoding_enabled": true
      },
      "text": {
        "passed": true
      },
      "tool": {
        "passed": true
      },
      "vision": {
        "passed": true
      }
    },
    "hardware": {
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      "memory_GiB": 48,
      "model": "Mac M4 Pro"
    },
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  "schema": 1,
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  }
}

The bounded WikiText-2 comparison is quantization-fidelity evidence, not an intelligence benchmark. Throughput depends on hardware, context, prompt, and runtime. M4 Pro load/generation tests must cover MTP off and on before publication.

Legacy release history

The prior public revision was quantized from an FP8-expanded BF16 working representation. That historical lineage does not describe this replacement.

Как запустить

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

GPU в ailist

Универсальный рантайм · mlx

Нельзя запустить

MLX-модели работают только на Apple Silicon, на GPU NVIDIA они не запускаются.

Класс архитектуры
Qwen3_5ForConditionalGeneration
Ревизия весов
c3de763

0 моделей