rasbt / ai-text-detector-qwen3-0.6b-variable

Qwen3-0.6B Variable-Position AI-Text Detector

This is a fully fine-tuned Qwen3-0.6B classifier for distinguishing human-written and AI-generated text. It uses a variable-position readout token immediately after the input text. The model was trained on rasbt/human-vs-ai-50k . Human-written text has label 0 and AI-generated text has label 1.

The model was trained with a maximum context length of 1,024 tokens. Temperature scaling is applied during inference. The recorded best validation accuracy was 99.92%.

Download and use

hf download rasbt/ai-text-detector-qwen3-0.6b-variable \
  --local-dir models/ai-text-detector-qwen3-0.6b-variable

import json
from pathlib import Path

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer


model_dir = Path("models/ai-text-detector-qwen3-0.6b-variable")
metadata = json.loads(
    (model_dir / "detector-config.json").read_text(encoding="utf-8")
)
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForSequenceClassification.from_pretrained(model_dir)
model.eval()

text = "Paste the text to classify here."
text_ids = tokenizer(
    text,
    add_special_tokens=False,
    truncation=True,
    max_length=metadata["max_text_length"],
)["input_ids"]

if metadata["readout_position"] == "fixed":
    padding_length = metadata["context_length"] - len(text_ids) - 1
    input_ids = (
        text_ids
        + [tokenizer.pad_token_id] * padding_length
        + [tokenizer.eos_token_id]
    )
    attention_mask = [1] * len(text_ids) + [0] * padding_length + [1]
else:
    input_ids = text_ids + [tokenizer.eos_token_id]
    attention_mask = [1] * len(input_ids)

inputs = {
    "input_ids": torch.tensor([input_ids]),
    "attention_mask": torch.tensor([attention_mask]),
}
with torch.inference_mode():
    logits = model(**inputs).logits / metadata["temperature"]
    probabilities = logits.float().softmax(dim=-1)

ai_index = metadata["label_mapping"]["ai"]
ai_probability = probabilities[0, ai_index].item()
print({"score": round(100 * ai_probability, 4)})

Test-set confusion matrix

detector-config.json contains the readout, calibration, and training metadata. The recommended inference implementation is provided in the rasbt/ai-detector repository because classification requires selecting the configured readout position.

Related models

• TF-IDF logistic regression
• DistilBERT
• DistilBERT with LoRA
• DistilBERT with MiCA
• ModernBERT
• GPT-2 with a fixed-position readout
• GPT-2 with a variable-position readout
• Qwen3 0.6B with a fixed-position readout

Limitations

Performance may change for text from generators, domains, languages, and editing workflows not represented in the training set. Short or partly AI-assisted text may also be harder to classify. The score should not be treated as definitive evidence that a person did or did not write a text.