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: apache-2.0 language:

ru
en
uk
be library_name: transformers tags:
- qwen3
- qwen3-119-languages
- supports-119-languages
- multilingual
- language-support
awq
int4
quantization
russian
qwen3
noesis
dhcf-fno base_model: t-tech/T-pro-it-2.1 quantized_by: AMAImedia pipeline_tag: text-generation

Qwen3-32B-T-pro-it-2.1-NOESIS-AWQ-INT4

AWQ INT4 quantization of t-tech/T-pro-it-2.1 optimized for low-VRAM consumer hardware via streaming inference.

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

License: Apache-2.0 (inherited from base model — fully permissive, commercial use allowed)

ℹ️ Architecture clarification

T-pro-it-2.1 is a dense Qwen3-32B model, NOT a Mixture-of-Experts (MoE). Upstream training used a SLERP merge of three GRPO-trained experts as a training-time technique, but the resulting checkpoint is a single set of dense weights with one forward pass and no router. This release follows that architecture exactly — there are no expert layers, no gating networks, and no conditional computation.

Model summary

Property	Value
Base model	t-tech/T-pro-it-2.1
Underlying architecture	Qwen3-32B (decoder-only transformer, 64 layers, dense)
Original precision	BF16 safetensors (~64 GB)
Quantized precision	AWQ INT4 (group_size=128, GEMM, zero_point=True)
Vocab size	151936
Languages	Russian (primary), English, Ukrainian, Belarusian
Disk footprint	~8.5 GB
Inference VRAM (full-resident)	~9 GB (does NOT fit 6 GB GPUs without streaming)
Inference VRAM (streaming)	~3.4 GB peak (per-layer offload — fits 6 GB GPU)
Quantization library	AutoAWQ 0.2.9
Calibration set	128 prompts (70% RU / 20% EN / 10% code), max_seq_len=512
RNG seed	1729 (NOESIS reproducibility lock)

Key feature: 6 GB GPU compatibility via streaming

Standard AWQ-INT4 of a 32B model needs ~9 GB VRAM, which excludes RTX 3060 / 4060 class hardware. NOESIS ships a per-layer weight-streaming inference path where individual transformer layers are streamed from CPU RAM onto the GPU on demand, executed, and freed. Peak VRAM stays at ~3.4 GB, well inside the SEALED 4.5 GB NOESIS specialist window.

Throughput on RTX 3060 (i7-12700H, DDR5-4800):

Prefill: ~25 tok/s
Per-layer load overhead: ~7 ms × 64 layers = 0.45 s amortized per batch

Suitable for: KD logits extraction, batch inference, offline summarization. For low-latency interactive chat use the same checkpoint on a 12 GB+ GPU in standard AutoAWQ inference mode.

How to use

Standard inference (12 GB+ GPU):

python

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
import torch

model_id = "amaimedia/Qwen3-32B-T-pro-it-2.1-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,
)

messages = [
    {"role": "system", "content": "Ты T-pro, полезный ассистент."},
    {"role": "user", "content": "Объясни принцип работы трансформера."},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to("cuda")
out = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7, top_p=0.8, top_k=20,
    repetition_penalty=1.0,
)
print(tokenizer.decode(out[0], skip_special_tokens=True))

Recommended generation parameters per upstream T-Tech guidance: temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.0. Both temperature and presence_penalty should be set explicitly.

Streaming inference (6 GB GPU): see the NOESIS extract_kd_streaming.py reference implementation.

NOESIS context

In NOESIS this model serves as the Russian-language teacher for several specialists during knowledge distillation:

Target specialist	Role	Proposed KD weight
M2-DUB-LM-10B	Dubbing LM (Russian segments)	0.18
M4-CHAT-10B	Chat / creative writing (Russian)	0.18
M9-ORCH-4B	Orchestrator (Russian routing)	0.15

Vocab match (151936) is identical to the NOESIS base (Qwen3-8B), enabling direct logit alignment without cross-tokenizer projection — a critical property for clean KD on Russian shards.

Quantization details

Calibration distribution:

70% Russian: chat, technical instruction, scientific exposition, creative writing
20% English: technical & instructional
10% Code: Python, Rust (RU and EN comments)

Quantization performed on:

CPU: Intel i7-12700H (14 cores)
RAM: 64 GB DDR5
GPU: RTX 3060 6 GB (per-layer scale search)
Disk offload: NVMe (B:\noesis_offload_tpro\, freed after quantization)

Wall time: ~3.5 hours.

Acknowledgements & citation

Base model:

bibtex

@misc{stoianov2025tpro20,
  title  = {T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground},
  author = {Dmitrii Stoianov and Danil Taranets and Olga Tsymboi and others},
  year   = {2025},
  eprint = {2512.10430},
  archivePrefix = {arXiv}
}

Quantization & NOESIS integration: