AMAImedia

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

README

License: apache-2.0

Specifications

Table with columns: Field, Value
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

Table with columns: Parameter, Value
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)

text

Load:    10.4 sGen:     1.6 s (20 tokens)VRAM:    8.01 GB peakOutput:  "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)

python

import torchfrom 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)

Table with columns: Phase, RAM, VRAM, Time
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

bibtex

@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}}

Produced 2026-05-17 / 2026-05-18 by NOESIS DHCF-FNO v15.7 — AMAImedia.com

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Model Details

Model Provider

AMAImedia

Model Tree

Base

Qwen/Qwen3Guard-Gen-8B

Quantized

this model

Input Modalities

Text

Output Modalities