Text Classification
Transformers
Safetensors
English
Japanese
Chinese
qwen3_5_text
ai-generated-text-detection
int4
torchao
Instructions to use yaoandy107/greyscope-v2-qwen3.5-4b-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yaoandy107/greyscope-v2-qwen3.5-4b-int4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yaoandy107/greyscope-v2-qwen3.5-4b-int4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yaoandy107/greyscope-v2-qwen3.5-4b-int4") model = AutoModelForSequenceClassification.from_pretrained("yaoandy107/greyscope-v2-qwen3.5-4b-int4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Greyscope v2 int4
This is the 3.5 GB Transformers int4-HQQ build of Greyscope v2. It returns the same continuous
ai_involvement score and human / AI-edited / AI-generated labels as the
bf16 model.
Use this build when you need Transformers but cannot fit bf16. Do not use it on Apple Silicon; use MLX 4-bit.
Quick start
git clone https://github.com/yaoandy107/greyscope
cd greyscope
uv sync --extra int4
uv run greyscope --model int4 "Paste a paragraph here."
Quantization check
The release export checked this artifact against bf16 on 64 rows. Treat this as a smoke test, not a quality benchmark, and validate it on your own data and hardware.
Evaluations, calibration, training details, and limitations are documented on the bf16 model card.
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Model tree for yaoandy107/greyscope-v2-qwen3.5-4b-int4
Base model
Qwen/Qwen3.5-4B-Base Finetuned
unsloth/Qwen3.5-4B-Base Finetuned
yaoandy107/greyscope-v2-qwen3.5-4b