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Model: AMAImedia/Qwen3-8B-Nemotron-Orchestrator-NOESIS-AWQ-INT4
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---
license: other
license_name: nvidia-open-model-license
license_link: LICENSE
language:
- en
library_name: transformers
tags:
- awq
- int4
- quantization
- 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-AWQ-INT4
**AWQ INT4 quantization of [nvidia/Nemotron-Orchestrator-8B](https://huggingface.co/nvidia/Nemotron-Orchestrator-8B)
optimized for low-VRAM consumer hardware (RTX 3060 6 GB).**
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 AWQ derivative is published to make the model accessible to the broader
research and development community on consumer GPUs. **Users are responsible
for compliance with NVIDIA's license terms** — see the `LICENSE` file in
this repository for the full text.
By downloading or using this model you agree to the upstream NVIDIA license.
---
## Model summary
| Property | Value |
| --- | --- |
| Base model | nvidia/Nemotron-Orchestrator-8B |
| Underlying architecture | Qwen3-8B (decoder-only transformer, **dense, NOT MoE**) |
| Original precision | FP32 safetensors (~32 GB) |
| Quantized precision | AWQ INT4 (group_size=128, GEMM, zero_point=True) |
| Vocab size | 151936 |
| Language | English (per base model) |
| Disk footprint | ~4.5 GB |
| Inference VRAM | ~5.0 GB (full-resident on 6 GB GPU) |
| Quantization library | AutoAWQ 0.2.9 |
| Calibration set | 128 in-house orchestration / tool-calling prompts, max_seq_len=512 |
| RNG seed | 1729 (NOESIS reproducibility lock) |
A companion **BF16 reference checkpoint** is also published:
[amaimedia/Nemotron-Orchestrator-8B-Qwen3-BF16-NOESIS](https://huggingface.co/amaimedia/Nemotron-Orchestrator-8B-Qwen3-BF16-NOESIS).
---
## Why this quantization
The original Nemotron-Orchestrator-8B is shipped in **FP32 (~32 GB on disk)** which
does not fit any consumer GPU. Existing community quantizations exist (mostly
GGUF) but none is calibrated specifically for orchestration / tool-calling
scale search and packaged for the **AutoAWQ GEMM kernel path** that integrates
directly with `transformers` and `vllm` on Windows hosts.
This AWQ build:
1. Fits inside the **4.5 GB SEALED VRAM window** of the NOESIS specialist
sequential-swapping protocol
2. Uses **GEMM kernel** (compatible with `device_map={"":0}` — no CPU offload)
3. Provenance-tracked (`noesis_provenance.json` ships with the model)
4. Calibrated on orchestration / tool-calling prompts matching base model
training distribution (ToolScale + GeneralThought-430K)
---
## How to use
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
import torch
model_id = "amaimedia/Qwen3-8B-Nemotron-Orchestrator-NOESIS-AWQ-INT4"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoAWQForCausalLM.from_quantized(
model_id,
device_map={"": 0},
torch_dtype=torch.float16,
fuse_layers=False,
)
prompt = "Plan a multi-step task: search for recent AWQ papers, then summarize."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(out[0], skip_special_tokens=True))
```
---
## NOESIS context
In NOESIS this model serves as the **English orchestration teacher** for
Specialist **M9-ORCH-4B** during knowledge distillation. It is loaded
sequentially (per the NOESIS swapping protocol) onto the RTX 3060,
producing top-K=512 logits at temperature=4.0, which are then aggregated
in `build_ensemble_labels.py` with proposed weight **w=0.22** on the
orchestration data shard.
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** |
---
## 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}
}
```
Quantization & NOESIS integration:
```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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{%- 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 %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- 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 %}

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": null,
"dtype": "bfloat16",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 12288,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
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"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 40960,
"max_window_layers": 36,
"model_type": "qwen3",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"pad_token_id": 151643,
"quantization_config": {
"bits": 4,
"group_size": 128,
"modules_to_not_convert": null,
"quant_method": "awq",
"version": "gemm",
"zero_point": true
},
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "5.6.0.dev0",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 151936
}

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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"temperature": 0.6,
"top_k": 20,
"top_p": 0.95,
"transformers_version": "5.6.0.dev0"
}

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"metadata": {
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