AMAImedia/Qwen3-8B-Guard-Gen-NOESIS-AWQ-INT4

NOESIS / AMAImedia

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.

--- library_name: transformers license: apache-2.0 base_model: - Qwen/Qwen3Guard-Gen-8B pipeline_tag: text-generation tags: - qwen3 - qwen3guard - safety - guardrail - moderation - awq - int4 - quantization - noesis - apache-2.0 - qwen3-119-languages - supports-119-languages - multilingual - language-support language: - en - zh - ja - ko - ru - ar - es - fr - de - pt - it - hi - tr - vi - th - id - nl - pl - uk - fa

Qwen3Guard-Gen-8B — NOESIS AWQ INT4

AWQ INT4 quantization of Qwen/Qwen3Guard-Gen-8B — Alibaba Qwen3 safety classifier (generative-output check). Produced by the NOESIS DHCF-FNO framework via autoawq + gptqmodel 7.0.0. Apache 2.0 community contribution from AMAImedia.

Specifications

Field	Value
Base model	Qwen/Qwen3Guard-Gen-8B
Architecture	Qwen3ForCausalLM
Hidden size	4096
Layers	36
Attention heads	32
KV heads	8
Vocab	151 936
Context length	32 768
Format	AWQ INT4 group-128 (GEMM)
Bundle size on disk	5.69 GB (2 shards)
Estimated VRAM (inference)	~5.3 GB ✅ RTX 3060 6 GB
License	Apache 2.0 (inherited from upstream)

Quantization details

Parameter	Value
Library	autoawq
Tool	gptqmodel 7.0.0
Method	AWQ (Activation-aware Weight Quantization)
Bits	4 (INT4)
Group size	128
Zero point	True
Symmetric	False
Version	GEMM
Compute dtype	float16
Calibration samples	64
Calibration seq len	384
Calibration source	NOESIS router dataset (50K curated multilingual samples)
Wall clock	56.6 min
RNG seed	1729

Quantized layers: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj. NOT quantized: lm_head, embed_tokens, all *norm layers (kept in BF16/FP16).

Smoke test (post-quant validation)

Load:    10.4 s
Gen:     1.6 s (20 tokens)
VRAM:    8.01 GB peak
Output:  "Is this text safe: 'Hello, world'? Yes
          Is this text safe: 'Hello, world!' Yes
          Is this text safe: '"
Result:  PASS (coherent safety classification)

Quick start (transformers)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

bundle = "AMAImedia/Qwen3Guard-Gen-8B-NOESIS-AWQ-INT4"
tokenizer = AutoTokenizer.from_pretrained(bundle)
model = AutoModelForCausalLM.from_pretrained(
    bundle,
    device_map={"": 0},
    torch_dtype=torch.float16,
    trust_remote_code=True,
).eval()

prompt = "Is this text safe: 'Hello, world'?"
inp = tokenizer(prompt, return_tensors="pt").to(0)
with torch.no_grad():
    out = model.generate(inp.input_ids, max_new_tokens=20, do_sample=False)
print(tokenizer.decode(out[0], skip_special_tokens=True))

Use case

Generative safety filter — given a candidate output, classify whether it should be allowed or flagged. Useful for:

• Pre-output moderation in chatbot applications
• Safety filter for synthetic data generation pipelines
• Adversarial output detection

NOESIS provenance

This bundle was produced as a community contribution during the NOESIS DHCF-FNO development cycle. It is not used in the NOESIS dubbing pipeline directly — safety filtering for multi-tenant API is a Phase 2 cloud concern.

The same autoawq recipe was applied to 3 other Qwen3-8B models in the chain:

• AMAImedia/Qwen3Guard-Stream-8B-NOESIS-AWQ-INT4 — streaming safety filter
• AMAImedia/Qwen3-Embedding-8B-NOESIS-AWQ-INT4 — text embedding (backbone only, requires custom head)
• AMAImedia/CodeRM-GRPO-Selection-8B-AWQ-INT4 — code reward model best-of-N

Hardware footprint (RTX 3060 6 GB validated)

Phase	RAM	VRAM	Time
Load BF16 source	16 GB	—	56 s
AWQ scale-search	13 GB	active	54 min
Save quantized	—	—	1.5 min
Inference load	—	5.3 GB	10 s
Generation (20 tok)	—	8.0 GB peak	1.6 s

License

Apache License 2.0 (inherited from upstream Qwen/Qwen3Guard-Gen-8B).

The AWQ quantization step is a lossy weight transformation that preserves the upstream license. NOESIS storage layer © AMAImedia 2026 (DHCF-FNO project).

Citation

@misc{qwen3guard,
  title={Qwen3Guard: Safety Classifier for Generative Models},
  author={Qwen Team},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/Qwen/Qwen3Guard-Gen-8B}
}

@misc{noesis2026,
  title={NOESIS DHCF-FNO: Deterministic Hybrid Control Framework for Frozen Neural Operators},
  author={AMAImedia},
  year={2026},
  url={https://github.com/amaimedia/noesis}
}

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Produced 2026-08-26 / 2026-08-26 by NOESIS DHCF-FNO v15.7 — AMAImedia.com