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

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

**BF16 reference checkpoint of [nvidia/Nemotron-Orchestrator-8B](https://huggingface.co/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).

- **Founder:** Ilia Bolotnikov
- **Organization:** [AMAImedia.com](https://www.amaimedia.com)
- **X (Twitter):** [@AMAImediacom](https://x.com/AMAImediacom)
- **LinkedIn:** [Ilia Bolotnikov](https://www.linkedin.com/in/ilia-bolotnikov)
- **Telegram:** [@djbionicl](https://t.me/djbionicl)
- **NOESIS version:** v14.6
- **Release date:** 2026-04

---

## ⚠️ 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.

---

## 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.

---

## 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](https://huggingface.co/amaimedia/Nemotron-Orchestrator-8B-Qwen3-AWQ-INT4-NOESIS).

---

## How to use

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "amaimedia/Nemotron-Orchestrator-8B-Qwen3-BF16-NOESIS"
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))
```

---

## 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.

---

## Acknowledgements & citation

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

```bibtex
@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:

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