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---
license: apache-2.0
language:
- en
- ru
library_name: gigacheck
tags:
- token-classification
- detr
- ai-detection
- multilingual
- gigacheck
datasets:
- iitolstykh/LLMTrace_detection
base_model:
- mistralai/Mistral-7B-v0.3
---
# GigaCheck-Detector-Multi
<p style="text-align: center;">
<div align="center">
<img src="https://raw.githubusercontent.com/sweetdream779/LLMTrace-info/refs/heads/main/images/logo/GigaCheck-detector-multi.PNG" width="40%"/>
</div>
<p align="center">
<a href="https://sweetdream779.github.io/LLMTrace-info"> 🌐 LLMTrace Website </a> |
<a href="http://arxiv.org/abs/2509.21269"> 📜 LLMTrace Paper on arXiv </a> |
<a href="https://huggingface.co/datasets/iitolstykh/LLMTrace_detection"> 🤗 LLMTrace - Detection Dataset </a> |
<a href="https://github.com/ai-forever/gigacheck"> Github </a> |
</p>
## Model Card
### Model Description
This is the official `GigaCheck-Detector-Multi` model from the `LLMTrace` project. It is a multilingual transformer-based model trained for **AI interval detection**. Its purpose is to identify and localize the specific spans of text within a document that were generated by an AI.
The model was trained jointly on the English and Russian portions of the `LLMTrace Detection dataset`, which includes human, fully AI, and mixed-authorship texts with character-level annotations.
For complete details on the training data, methodology, and evaluation, please refer to our research paper: link(coming soon)
### Intended Use & Limitations
This model is intended for fine-grained analysis of documents, academic integrity tools, and research into human-AI collaboration.
**Limitations:**
* The model's performance may degrade on text generated by LLMs released after its training date (September 2025).
* It is not infallible and may miss some AI-generated spans or incorrectly flag human-written parts.
* The boundary predictions may not be perfectly precise in all cases.
## Evaluation
The model was evaluated on the test split of the `LLMTrace Detection dataset`. The performance is measured using standard mean Average Precision (mAP) metrics for object detection, adapted for text spans.
| Metric | Value |
|---------------|--------|
| mAP @ IoU=0.5 | 0.8976 |
| mAP @ IoU=0.5:0.95 | 0.7921 |
## Quick start
Requirements:
- python3.11
- [gigacheck](https://github.com/ai-forever/gigacheck)
```bash
pip install git+https://github.com/ai-forever/gigacheck
```
### Inference with transformers (with trust_remote_code=True)
```python
from transformers import AutoModel
import torch
model_name = "iitolstykh/GigaCheck-Detector-Multi"
gigacheck_model = AutoModel.from_pretrained(
model_name, trust_remote_code=True, device_map="cuda:0", torch_dtype=torch.float32
)
text = "The critic's review of the recent publication was scathing. The book failed miserably in portraying the harmful subjective discourses associated with the hegemony of the political system."
output = gigacheck_model([text], conf_interval_thresh=0.5)
# [(start_char, end_char, score)]
print(output.ai_intervals)
```
### Inference with gigacheck
```python
from transformers import AutoConfig
from gigacheck.inference.src.mistral_detector import MistralDetector
import torch
model_name = "iitolstykh/GigaCheck-Detector-Multi"
config = AutoConfig.from_pretrained(model_name)
model = MistralDetector(
max_seq_len=config.max_length,
with_detr=config.with_detr,
id2label=config.id2label,
device="cpu" if not torch.cuda.is_available() else "cuda:0",
conf_interval_thresh=0.5,
).from_pretrained(model_name)
text = "The critic's review of the recent publication was scathing. The book failed miserably in portraying the harmful subjective discourses associated with the hegemony of the political system."
output = model.predict(text)
print(output)
```
## Citation
If you use this model in your research, please cite our papers:
```bibtex
@article{Layer2025LLMTrace,
Title = {{LLMTrace: A Corpus for Classification and Fine-Grained Localization of AI-Written Text}},
Author = {Irina Tolstykh and Aleksandra Tsybina and Sergey Yakubson and Maksim Kuprashevich},
Year = {2025},
Eprint = {arXiv:2509.21269}
}
@article{tolstykh2024gigacheck,
title={{GigaCheck: Detecting LLM-generated Content}},
author={Irina Tolstykh and Aleksandra Tsybina and Sergey Yakubson and Aleksandr Gordeev and Vladimir Dokholyan and Maksim Kuprashevich},
journal={arXiv preprint arXiv:2410.23728},
year={2024}
}
``` |