/ Модель

Характеристики

Контекст
262 144 токена
Размер весов
17,9 ГБ
Архитектура
qwen3_5 · dense
Библиотека
mlx
Опубликована
27 авг 2026

NOESIS-Qwopus3.5-9B-DubCompress-v3.5-MLX

Генерация текстаТекст → текст3 профессии

Специализированная модель AMAImedia на базе Qwen3.5 сжимает и преобразует многоязычный текст для дубляжа, перевода и подгонки под субтитры или временные ограничения.

118скачиваний за 30 дней0лайковвеса обновлены28 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

NOESIS-Qwopus3.5-9B-DubCompress-v3.5-MLX — специализированная языковая модель AMAImedia для автоматизации многоязычного дубляжа. Это MLX-представление оригинальной модели NOESIS, обученной на базе плотной архитектуры Qwen3.5 с 9 млрд параметров; веса сохранены в формате safetensors для запуска через mlx-lm на Apple Silicon. Заявленный контекст составляет 262 144 токена, однако фактическая длина зависит от доступной объединённой памяти и настроек KV-кэша.

Модель предназначена для сжатия диалогов, постобработки переводов и подгонки текста под ограничения субтитров или длительности озвучивания. В запросе следует задавать целевой язык, бюджет символов или времени, сохраняемый смысл и требования к формату. Семейство заявляет поддержку 201 языка и диалекта; качество сжатия может различаться между языками. Решение ориентировано на локальную работу с MLX на Mac с Apple Silicon. README не сообщает лицензию, поэтому условия использования по имеющимся сведениям определить нельзя.

Сценарии применения

  • сжатие переведённых реплик под лимит символов субтитров
  • адаптация текста дубляжа под заданную длительность озвучивания
  • постобработка перевода для сохранения смысла при сокращении
  • подготовка многоязычных диалогов для дубляжного конвейера

Ограничения

Качество сжатия и естественность результата могут различаться по языкам; перед применением нужно проверять сохранение смысла, длину и соответствие таймингу. Для запуска требуются Apple Silicon, MLX и память сверх объёма весов; лицензия в предоставленных данных не указана.

Для каких профессий подходит3

Разделы каталога профессий, в задачах которых используется эта модель

Все профессии

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

NOESIS / AMAImedia

Last updated: 2026-08-29

Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators).

NOESIS-Qwopus3.5-9B-DubCompress-v3.5-MLX

Original NOESIS MLX DubCompress specialist for multilingual dialogue compression, translation post-processing, dubbing transformations, and fitting translated text into fixed subtitle or speech-duration slots. This repository contains the MLX safetensors conversion of the BF16-trained NOESIS-Qwopus3.5-9B-DubCompress-v3.5 model for Apple Silicon inference through mlx-lm.

This is the MLX counterpart of the published NOESIS-Qwopus3.5-9B-DubCompress-v3.5-BF16 model card. The BF16 repository is the primary Transformers/GGUF distribution; this repository is the local MLX distribution of the same original NOESIS-trained specialist.

Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators).

PropertyValue
Model roleDubbing compression, translation post-processing, and subtitle-slot fitting
ArchitectureQwen3_5ForCausalLM, qwen3_5
ParametersApproximately 9B
Layers / hidden size32 / 4096
Intermediate size12288
Context limit in config262,144 positions
Stored dtypeBF16 configuration; MLX safetensors
Weight shards4 files, approximately 17.93 GB total
TokenizerQwen3.5 tokenizer with chat_template.jinja
Runtimemlx-lm on Apple Silicon
NOESIS versionv16.1
Release date2026-08-26

Language support

The Qwen3.5 family advertises expanded coverage of 201 languages and dialects. The upstream Qwen3 language list below is the transparent documented baseline used for this derivative; the published Qwen3.5 count is broader than the enumerated list.

English, French, Portuguese, German, Romanian, Swedish, Danish, Bulgarian, Russian, Czech, Greek, Ukrainian, Spanish, Dutch, Slovak, Croatian, Polish, Lithuanian, Norwegian Bokmål, Norwegian Nynorsk, Persian, Slovenian, Gujarati, Latvian, Italian, Occitan, Nepali, Marathi, Belarusian, Serbian, Luxembourgish, Venetian, Assamese, Welsh, Silesian, Asturian, Chhattisgarhi, Awadhi, Maithili, Bhojpuri, Sindhi, Irish, Faroese, Hindi, Punjabi, Bengali, Oriya, Tajik, Eastern Yiddish, Lombard, Ligurian, Sicilian, Friulian, Sardinian, Galician, Catalan, Icelandic, Tosk Albanian, Limburgish, Dari, Afrikaans, Macedonian, Sinhala, Urdu, Magahi, Bosnian, Armenian; Chinese (Simplified Chinese, Traditional Chinese, Cantonese), Burmese; Arabic (Standard, Najdi, Levantine, Egyptian, Moroccan, Mesopotamian, Ta’izzi-Adeni, Tunisian), Hebrew, Maltese; Indonesian, Malay, Tagalog, Cebuano, Javanese, Sundanese, Minangkabau, Balinese, Banjar, Pangasinan, Iloko, Waray (Philippines); Tamil, Telugu, Kannada, Malayalam; Turkish, North Azerbaijani, Northern Uzbek, Kazakh, Bashkir, Tatar; Thai, Lao; Finnish, Estonian, Hungarian; Vietnamese, Khmer; Japanese, Korean, Georgian, Basque, Haitian, Papiamento, Kabuverdianu, Tok Pisin, Swahili.

Language coverage is inherited from the Qwen3.5/Qwen3 family and should not be interpreted as identical compression quality for every language. NOESIS production use must validate semantic preservation, target-language naturalness, subtitle length, syllable density, and timing budget.

Repository contents

NOESIS-Qwopus3.5-9B-DubCompress-v3.5-MLX/
├── README.md
├── model-00001-of-00004.safetensors  # MLX shard 1
├── model-00002-of-00004.safetensors  # MLX shard 2
├── model-00003-of-00004.safetensors  # MLX shard 3
├── model-00004-of-00004.safetensors  # MLX shard 4
├── model.safetensors.index.json
├── config.json
├── generation_config.json
├── tokenizer.json
├── tokenizer_config.json
└── chat_template.jinja

The four shards and tokenizer files must remain together. Do not rename shards or remove model.safetensors.index.json; the MLX loader uses the index to reconstruct the complete model.

How to run with MLX-LM

Important platform note

mlx-lm is designed for Apple Silicon and uses the MLX backend. The B:\Downloads\... path is the Windows storage location of this repository; it is not a native Windows execution path for MLX. Copy or sync the repository to an Apple Silicon Mac, then use a macOS path such as /Users/ilia/Models/NOESIS-Qwopus3.5-9B-DubCompress-v3.5-MLX.

Install

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -U pip mlx-lm

Single prompt generation

DubCompress should receive the source or draft translation together with explicit constraints. Do not ask it to compress text without telling it the target language, the available duration or character budget, and the meaning that must be preserved.

python -m mlx_lm.generate \
  --model /Users/ilia/Models/NOESIS-Qwopus3.5-9B-DubCompress-v3.5-MLX \
  --prompt "Target language: Russian. Slot budget: 4.2 seconds. Preserve names and meaning. Compress this dialogue to fit: Welcome to the Sanctuary, Miss Amelia." \
  --max-tokens 256 \
  --temp 0.3

For deterministic pipeline trials, use a low temperature and a bounded token limit. For long subtitle batches, reduce --prefill-step-size if memory spikes and set --max-kv-size to a deliberate bound.

Chat mode

python -m mlx_lm.chat \
  --model /Users/ilia/Models/NOESIS-Qwopus3.5-9B-DubCompress-v3.5-MLX

Use the supplied chat_template.jinja through mlx-lm. A production prompt should identify the source text, target language, duration/character budget, forbidden changes, and the expected output format.

OpenAI-compatible local server

python -m mlx_lm.server \
  --model /Users/ilia/Models/NOESIS-Qwopus3.5-9B-DubCompress-v3.5-MLX \
  --host 127.0.0.1 \
  --port 8080

Request it from another terminal:

curl http://127.0.0.1:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "system", "content": "You are NOESIS DubCompress. Preserve meaning, names, tone, and target-language naturalness while fitting the stated slot."},
      {"role": "user", "content": "Target language: Spanish. Slot: 3.5 seconds. Return only the compressed line. Source: Welcome to the Sanctuary, Miss Amelia."}
    ],
    "temperature": 0.3,
    "max_tokens": 256
  }'

The MLX-LM server provides a basic OpenAI-compatible local endpoint. It is intended for local development and pipeline experiments and should not be exposed directly to the public Internet.

Python API

from mlx_lm import load, generate

model_dir = "/Users/ilia/Models/NOESIS-Qwopus3.5-9B-DubCompress-v3.5-MLX"
model, tokenizer = load(model_dir)

messages = [
    {
        "role": "system",
        "content": (
            "You are NOESIS DubCompress. Preserve meaning, names, tone, "
            "and target-language naturalness while fitting explicit timing constraints."
        ),
    },
    {
        "role": "user",
        "content": (
            "Target language: Russian. Slot budget: 4.2 seconds. "
            "Return only the compressed dialogue line. "
            "Source: Welcome to the Sanctuary, Miss Amelia."
        ),
    },
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
answer = generate(
    model,
    tokenizer,
    prompt=prompt,
    max_tokens=256,
    temp=0.3,
    verbose=True,
)
print(answer)

NOESIS dubbing workflow

DubCompress is not a generic summarizer. It is a constrained language transformation stage in the NOESIS dubbing pipeline:

source transcript / translation
  → target-language instruction
  → slot duration, syllable, or character budget
  → DubCompress candidate
  → semantic and named-entity check
  → multilingual ASR / pronunciation check
  → timing and supervisor gates
  → TTS and sample-accurate assembly

The model must never be allowed to silently delete names, negation, safety-critical facts, or plot information merely to meet a duration budget. If the budget is impossible, return a warning or escalate to the configured subtitle fallback rather than accepting a semantically damaged line.

TaskRecommended model
Fast sanity check and compact verdictInspector 0.8B MLX
Translation compression and subtitle-slot fittingDubCompress 9B MLX
Long-context review and final response judgmentSupervisor 9B MLX
Voice generationOmniVoice / Talker route, not DubCompress

Runtime and memory notes

ItemGuidance
WeightsApproximately 17.93 GB across four safetensors shards.
Unified memoryLeave additional memory for the OS, tokenizer, and KV cache; required memory is higher than the raw file size.
Context262,144 positions in configuration; practical context depends on Mac unified memory and KV-cache settings.
SamplingUse bounded max_tokens; low temperature is recommended for constrained rewriting.
Chat formatPreserve and use chat_template.jinja.
ServerBind to 127.0.0.1 for local-only use.
QualityValidate semantic preservation, target-language quality, and fit after every model call.

NOESIS sealed rules

  • R-DUB-COMPRESS-CONSTRAINED: every compression request must provide a target language and explicit slot or length constraint.
  • R-DUB-COMPRESS-SEMANTIC-PRESERVE: names, negation, plot facts, and speaker intent must survive compression.
  • R-DUB-COMPRESS-ASR-GATE: compressed candidates must pass multilingual ASR and pronunciation checks before TTS.
  • R-DUB-COMPRESS-SUPERVISOR-GATE: use Supervisor for difficult or ambiguous candidates; do not accept low-confidence rewrites silently.
  • R-MLX-LOCALHOST: the MLX-LM server is a local development service and must not be exposed without a separate secure gateway.
  • R-CHAT-TEMPLATE-PRESERVE: preserve the tokenizer and shipped chat template with all weight shards.
  • R-APACHE-CLEAN: preserve the applicable upstream and NOESIS license notices.

References

License

See the repository license and upstream notices. This MLX model is an original NOESIS-trained artifact released by AMAImedia; redistribution must preserve the model metadata and attribution.

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.

Класс архитектуры
Qwen3_5ForCausalLM
Ревизия весов
fb902ca

0 моделей