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---
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}
}
```

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---
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.**
This release halves the download bandwidth and disk footprint of the original model (from ~32 GB FP32 down to ~16 GB BF16) without any quality loss for inference, and serves as the source artifact for the AWQ INT4 quantization sibling.
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 · **Telegram:** [@djbionicl](https://t.me/djbionicl)
- **Organization:** [AMAImedia.com](https://amaimedia.com)
- **NOESIS version:** v14.1 · **Release date:** 2026-04
- **License:** NVIDIA Open Model License (research and development only — see `LICENSE`)
---
## NOESIS context
NOESIS is a 9-specialist multilingual video dubbing platform (full dubbing in 30 languages, ASR in 150+). 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 specialists overview:
| ID | Role | Size |
| --- | --- | --- |
| M1 | ASR (150+ langs) | 10B/3B |
| M2 | Dubbing LM (30 langs full) | 10B/3B |
| M3 | TTS + voice cloning | 10B/3B |
| M4 | Chat + creative writing | 10B/3B |
| M5 | Code + math | 10B/3B |
| M6 | Deep research (1M ctx) | 10B/3B |
| M7 | Prompt engineering | 4B/0.8B |
| M8 | Quality control (PRM) | 4B/0.8B |
| **M9** | **Orchestrator + routing** | **4B/0.8B** |
---
## Conversion details
| Property | Value |
| --- | --- |
| Source precision | FP32 (~32 GB) |
| This release precision | BF16 (~15.3 GB) |
| Conversion method | `torch.Tensor.to(dtype=torch.bfloat16)` |
| Rounding | IEEE 754 round-to-nearest-even (PyTorch default) |
| Quality impact | None for inference (BF16 has same 8-bit exponent as FP32) |
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))
```
---
## ⚠️ 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.
---
# Original Model Card
The following is the original model card from [nvidia/Nemotron-Orchestrator-8B](https://huggingface.co/nvidia/Nemotron-Orchestrator-8B), reproduced here for convenience. All credit for the underlying model goes to NVIDIA & the University of Hong Kong.
---
## ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration
[![Paper](https://img.shields.io/badge/ArXiv-Paper-brown)](https://arxiv.org/abs/2511.21689)
[![Code](https://img.shields.io/badge/GitHub-Link-orange)](https://github.com/NVlabs/ToolOrchestra/)
[![Model](https://img.shields.io/badge/HuggingFace-Model-green)](https://huggingface.co/nvidia/Nemotron-Orchestrator-8B)
[![Data](https://img.shields.io/badge/HuggingFace-Data-blue)](https://huggingface.co/datasets/nvidia/ToolScale)
[![Website](https://img.shields.io/badge/Web-Page-purple)](https://research.nvidia.com/labs/lpr/ToolOrchestra/)
### Description
Orchestrator-8B is a state-of-the-art 8B parameter orchestration model designed to solve complex, multi-turn agentic tasks by coordinating a diverse set of expert models and tools.
On the Humanity's Last Exam (HLE) benchmark, ToolOrchestrator-8B achieves a score of 37.1%, outperforming GPT-5 (35.1%) while being approximately 2.5x more efficient.
This model is for research and development only.
### Key Features
* **Intelligent Orchestration:** Capable of managing heterogeneous toolsets including basic tools (search, code execution) and other LLMs (specialized and generalist).
* **Multi-Objective RL Training:** Trained via Group Relative Policy Optimization (GRPO) with a novel reward function that optimizes for accuracy, latency/cost, and adherence to user preferences.
* **Efficiency:** Delivers higher accuracy at significantly lower computational cost compared to monolithic frontier models.
* **Robust Generalization:** Demonstrated ability to generalize to unseen tools and pricing configurations.
### Benchmark
On Humanity's Last Exam, Orchestrator-8B achieves 37.1%, surpassing GPT-5 (35.1%) with only 30% monetary cost and 2.5x faster. On FRAMES and τ²-Bench, Orchestrator-8B consistently outperforms strong monolithic systems, demonstrating versatile reasoning and robust tool orchestration.
Orchestrator-8B consistently outperforms GPT-5, Claude Opus 4.1 and Qwen3-235B-A22B on HLE with substantially lower cost.
### Model Details
* **Developed by:** NVIDIA & University of Hong Kong
* **Model Type:** Decoder-only Transformer (dense, not MoE)
* **Base Model:** [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)
* **Parameters:** 8B
* **Language(s):** English
* **License:** NVIDIA License
### Model Version
1.0
### Training Dataset
| Dataset | Link |
| --- | --- |
| GeneralThought-430K | [Link](https://huggingface.co/datasets/natolambert/GeneralThought-430K-filtered) |
| ToolScale | [Link](https://huggingface.co/datasets/nvidia/ToolScale) |
### Ethical Considerations
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail).
### Citation
```bibtex
@misc{toolorchestra,
title = {ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration},
author = {Hongjin Su and Shizhe Diao and Ximing Lu and Mingjie Liu and Jiacheng Xu and Xin Dong and Yonggan Fu and Peter Belcak and Hanrong Ye and Hongxu Yin and Yi Dong and Evelina Bakhturina and Tao Yu and Yejin Choi and Jan Kautz and Pavlo Molchanov},
year = {2025},
eprint = {2511.21689},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2511.21689}
}
```
---
## NOESIS citation
```bibtex
@misc{noesis_v14,
title = {NOESIS v14.1: DHCF-FNO Multilingual Dubbing Platform},
author = {Bolotnikov, Ilia},
year = {2026},
publisher = {AMAImedia},
url = {https://amaimedia.com}
}
```

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
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{%- else %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
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{%- else %}
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{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
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{%- endif %}
{{- '<|im_end|>\n' }}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

71
config.json Normal file
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"rounding": "IEEE 754 round-to-nearest-even (PyTorch default)",
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