NOESIS / AMAImedia

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

• Founder: Ilia Bolotnikov
• Organization: AMAImedia.com
• X (Twitter): @AMAImediacom
• LinkedIn: Ilia Bolotnikov
• Telegram: @djbionicl
• NOESIS version: v16.1
• Release date: 2026-08-26

Language support

This Qwen3-derived model follows the official Qwen3 language coverage (119 languages and dialects):

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.

--- license: other license_name: nvidia-open-model-license license_link: LICENSE language: - en library_name: transformers tags: - qwen3 - qwen3-119-languages - supports-119-languages - multilingual - language-support - bf16 - orchestration - tool-calling - noesis - dhcf-fno - qwen3 base_model: nvidia/Nemotron-Orchestrator-8B quantized_by: AMAImedia pipeline_tag: text-generation

Qwen3-8B-Nemotron-Orchestrator-NOESIS-BF16

BF16 reference checkpoint of nvidia/Nemotron-Orchestrator-8B, losslessly cast from the original FP32 release.

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

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⚠️ License notice

This model inherits the NVIDIA Open Model License from the upstream nvidia/Nemotron-Orchestrator-8B. The base model is designated by NVIDIA as "for research and development only".

This BF16 derivative is published as a bandwidth-friendly reference checkpoint for the broader research and development community. Users are responsible for compliance with NVIDIA's license terms — see the LICENSE file in this repository for the full text.

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Why this BF16 release exists

The original NVIDIA release ships in FP32 (~32 GB on disk). Most modern inference and quantization tooling (HuggingFace Transformers, vLLM, SGLang, AutoAWQ, AutoGPTQ, llama.cpp BF16 conversion) immediately casts to BF16 on load. Publishing a pre-cast BF16 checkpoint:

• Halves download bandwidth (16 GB vs 32 GB)
• Halves disk footprint
• Skips a slow load-time cast for users
• Provides a clean BF16 baseline for downstream quantization recipes

The cast is performed via torch.Tensor.to(dtype=torch.bfloat16) with IEEE 754 round-to-nearest-even (PyTorch default). BF16 has the same 8-bit exponent range as FP32 and 7 bits of mantissa, which is lossless for inference-time use of weight tensors.

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Model summary

Property	Value
Base model	nvidia/Nemotron-Orchestrator-8B
Underlying architecture	Qwen3-8B (decoder-only transformer, dense, NOT MoE)
Source precision	FP32
This release precision	BF16
Vocab size	151936
Language	English (per base model)
Disk footprint	~16 GB
Inference VRAM	~17 GB BF16 (full-resident on 24 GB+ GPU)

For low-VRAM (6-12 GB) inference, see the AWQ INT4 sibling release: amaimedia/Nemotron-Orchestrator-8B-Qwen3-AWQ-INT4-NOESIS.

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How to use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "amaimedia/Qwen3-8B-Nemotron-Orchestrator-NOESIS-BF16"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

prompt = "Plan a multi-step task: find recent AWQ papers, summarize the top three."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(out[0], skip_special_tokens=True))

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NOESIS context

This BF16 checkpoint is the source artifact for the AWQ INT4 quantization used as the English orchestration teacher for NOESIS Specialist M9-ORCH-4B during knowledge distillation.

NOESIS is a 9-specialist dubbing automation platform — see the NOESIS collection for the full specialist family.

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Acknowledgements & citation

Base model: ToolOrchestra by NVIDIA & University of Hong Kong.

@misc{toolorchestra,
  title  = {ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration},
  author = {Hongjin Su and Shizhe Diao and Ximing Lu and others},
  year   = {2025},
  eprint = {2511.21689},
  archivePrefix = {arXiv}
}

NOESIS:

@misc{noesis_v14,
  title  = {NOESIS v14.6: DHCF-FNO Multilingual Dubbing Platform},
  author = {Bolotnikov, Ilia},
  year   = {2026},
  publisher = {AMAImedia},
  url    = {https://amaimedia.com}
}