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 Qwen3.5-derived model follows the official Qwen3 language list below (119 languages and dialects) and the official Qwen3.5 coverage statement of 201 languages and dialects. Qwen3.5 publishes the expanded coverage count but does not provide an exhaustive 201-name enumeration in its model card. The list below is the complete language list published by Qwen for Qwen3 and is included as the transparent, documented baseline for this derivative.

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: apache-2.0 license_link: LICENSE language:

en
zh
ja
ko
de
fr
ru
ar
hi
es library_name: transformers tags:
- qwen3.5
- qwen3.5-201-languages
- supports-201-languages
- multilingual
- language-support
awq
int4
quantization
multimodal
vision-language
noesis
dhcf-fno
qwen3-5 base_model: FINAL-Bench/Darwin-9B-Opus quantized_by: AMAImedia pipeline_tag: image-text-to-text

Qwen3.5-9B-Darwin-Opus-NOESIS-AWQ-INT4

AWQ INT4 quantization of FINAL-Bench/Darwin-9B-Opus 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).

⚠️ License notice

This model is derived from FINAL-Bench/Darwin-9B-Opus which is licensed under Apache 2.0. This AWQ quantization retains the same Apache 2.0 license — see the LICENSE file in this repository for the full text.

Model summary

Property	Value
Base model	FINAL-Bench/Darwin-9B-Opus
Underlying architecture	Qwen3_5ForConditionalGeneration (VLM — text + vision encoder)
Model type	`qwen3_5`
Original precision	BF16 safetensors (~18 GB)
Quantized precision	AWQ INT4 (group_size=128, GEMM, zero_point=True)
Text vocab size	248 320
Context length	131 072 tokens
Hidden size (text)	4 096
Layers (text)	32 (hybrid: 24 GDN/linear_attention + 8 full_attention, interval=4)
Languages	201 (native Qwen3.5-9B multilingual)
Disk footprint	~4.7 GB
Inference VRAM	~5.2 GB (text-only path, no vision input)
Quantization library	AutoAWQ 0.2.9
Calibration set	128 diverse prompts (code/reasoning/chat/research), max_seq_len=512
RNG seed	1729 (NOESIS reproducibility lock)

Architecture note: Darwin-9B-Opus is a merge model built with the Darwin V5 methodology (MRI-guided per-tensor diagnostics 70% + evolutionary genome optimization 30%, implemented via direct PyTorch DARE-TIES).

Father: Qwen/Qwen3.5-9B (original pre-training + RLHF)

Mother: Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled (LoRA SFT on Claude 4.6 Opus reasoning chains)

The underlying Qwen3.5 backbone is the VLM branch (Qwen3_5ForConditionalGeneration) with a vision encoder and a hybrid text decoder (qwen3_5_text sub-config): 24 GatedDeltaNet/linear_attention layers + 8 full self-attention layers (full_attn at every 4th layer). AWQ quantization targets the text decoder weights only: GDN layers have their MLP quantized; full_attention layers get self_attn + MLP quantized.

Why this quantization

The original Darwin-9B-Opus weights are in BF16 (~18 GB) which does not fit a 6 GB consumer GPU. This AWQ build:

Fits inside ~5.2 GB VRAM for text-only inference on an RTX 3060 6 GB
Uses GEMM kernel — compatible with device_map={"":0} (no CPU offload)
Provenance-tracked (noesis_provenance.json ships alongside the model)
Calibrated on diverse multilingual prompts matching Darwin's broad training domain

Quantization methodology

This checkpoint was produced using a proprietary quantization pipeline developed by AMAImedia as part of the NOESIS DHCF-FNO framework (v14.7). The Qwen3.5 hybrid architecture used in Darwin-9B-Opus is not supported by upstream AutoAWQ; quantization required original engineering work developed internally at AMAImedia.

How to use

python

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
import torch

model_id = "amaimedia/Qwen3.5-9B-Darwin-Opus-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 = "Explain the difference between REST and GraphQL with code examples."
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))

Note: Vision inputs are not supported through AutoAWQ's text-only path. For multimodal use, load the BF16 base model with trust_remote_code=True.

NOESIS context

In NOESIS this model serves as a multilingual broad-domain teacher for Specialists M4-CHAT, M5-CODE, and M6-RESEARCH 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.

⚠️ KD pipeline note: Darwin-9B-Opus has vocab_size=248 320 (Qwen3.5 extended vocab), while NOESIS student models use Qwen3-8B native vocab 151 936. Logit extraction requires vocab head truncation to index 151 936 via purify_logits() before ensemble aggregation in build_ensemble_labels.py. Proposed KD weight: w=0.30.

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: Darwin-9B-Opus by FINAL-Bench (derived from Qwen3.5-9B + Claude Opus distillation).

bibtex

@misc{darwin9b_opus,
  title     = {Darwin-9B-Opus},
  author    = {FINAL-Bench},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/FINAL-Bench/Darwin-9B-Opus}
}

Quantization & NOESIS integration: