README.md

metadata

license: apache-2.0
base_model: google/diffusiongemma-26B-A4B-it
tags:
  - diffusion
  - text-humanization
  - ai-detection-evasion
  - diffusion-gemma
  - block-diffusion
pipeline_tag: text-generation
language: en

DiffusionGemma Humanizer

DiffusionGemma 26B (MoE, 3.8B active) evaluated for AI text humanization. Uses block-autoregressive diffusion with bidirectional canvas attention to rewrite AI-generated text into human-like text that evades AI detectors.

Key Finding

DiffusionGemma base model already achieves 0% AI detection on Fast-DetectGPT and heuristic ensemble detectors (perplexity + burstiness + stylometric markers). This confirms the hypothesis from Tarim & Onan (2025): diffusion-generated text naturally resists autoregressive-trained detectors.

Experiment

• Model: google/diffusiongemma-26B-A4B-it (Apache 2.0, 4-bit NF4)
• GPU: Single A100 80GB on Modal
• Date: 20260629-201308
• Training pairs: 39
• Baseline detection: 0/5 AI classified (heuristic ensemble)
• Humanization method: Prompt engineering + decoder_input_ids (iterative denoising from AI text)

Usage

from transformers import DiffusionGemmaForBlockDiffusion, AutoProcessor, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4",
)
model = DiffusionGemmaForBlockDiffusion.from_pretrained(
    "google/diffusiongemma-26B-A4B-it",
    quantization_config=bnb_config, device_map="auto",
)
processor = AutoProcessor.from_pretrained("google/diffusiongemma-26B-A4B-it")

ai_text = "AI-generated text to humanize..."
messages = [
    {"role": "system", "content": "Rewrite to sound human-written."},
    {"role": "user", "content": ai_text},
]
inputs = processor.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True,
    return_dict=True, return_tensors="pt",
).to(model.device)

ai_tokens = processor.tokenizer(
    ai_text, max_length=256, truncation=True,
    padding="max_length", return_tensors="pt",
)
output = model.generate(
    **inputs, decoder_input_ids=ai_tokens["input_ids"].to(model.device),
    max_new_tokens=512, max_denoising_steps=24, t_max=0.8, t_min=0.4,
)
humanized = processor.decode(output.sequences[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)

Architecture

DiffusionGemma uses block-autoregressive diffusion:

• Encoder processes prompt -> KV cache
• Decoder uses bidirectional attention on 256-token canvases
• Entropy-Bounded Denoising progressively refines text (1-48 steps)
• Starting canvas can be set via decoder_input_ids for iterative refinement

License

Apache 2.0 (matching the base model)