Instructions to use Aman90101/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aman90101/test with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Aman90101/test")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Aman90101/test")
model = AutoModelForCausalLM.from_pretrained("Aman90101/test")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Aman90101/test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Aman90101/test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aman90101/test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Aman90101/test

SGLang

How to use Aman90101/test with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Aman90101/test" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aman90101/test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Aman90101/test" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aman90101/test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Aman90101/test with Docker Model Runner:
```
docker model run hf.co/Aman90101/test
```

test / README.md

Aman90101

Duplicate from authormist/authormist-originality

08cf38d about 2 months ago

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	---
	language: en
	license: mit
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- text-generation
	- ai-detection
	- paraphrasing
	- originality
	- privacy
	datasets:
	- checkgpt
	base_model: Qwen/Qwen2.5-3B-Instruct
	model_type: causal-lm
	---

	# AuthorMist Originality

	[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-AuthorMist-blue)](https://huggingface.co/authormist/originality)
	[![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

	## Overview

	AuthorMist Originality is a specialized language model designed to transform AI-generated text into more human-like writing while preserving the original meaning. This model was developed using reinforcement learning techniques to specifically evade AI text detection systems, with a focus on Originality.ai's detection algorithms.

	The model is based on Qwen2.5-3B Instruct and has been fine-tuned using Group Relative Policy Optimization (GRPO) with detector feedback as a reward signal. AuthorMist Originality demonstrates strong performance in reducing detectability across multiple AI text detection systems while maintaining high semantic similarity with the original text.

	## Key Features

	- Detector Evasion: Trained specifically to evade Originality.ai's detection algorithms, with strong cross-detector generalization
	- Meaning Preservation: Maintains high semantic similarity (>0.94) with the original text
	- Natural Output: Produces fluent, coherent text that reads naturally
	- Broad Applicability: Effective across various domains including academic, technical, and creative writing

	## Model Details

	- Base Model: Qwen2.5-3B Instruct
	- Training Method: Reinforcement Learning with Group Relative Policy Optimization (GRPO)
	- Training Data: 10,000 human-written abstracts from the CheckGPT dataset with corresponding AI-generated versions
	- Domains Covered: Computer Science, Humanities, Social Sciences, Physics, and more
	- Text Length Support: Optimized for texts ranging from 100 to 500 words

	## Performance

	AuthorMist Originality demonstrates exceptional performance in evading AI text detection:

	- Mean AUROC: 0.49 across six major detection systems
	- Mean F1-score: 0.09 across all tested detectors
	- Semantic Similarity: >0.94 with original text

	The model shows particularly strong performance against:
	- Hello SimpleAI (AUROC: 0.07)
	- Sapling (AUROC: 0.13)
	- Winston.ai (AUROC: 0.35)

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	# Load model and tokenizer
	model_name = "authormist/authormist-originality"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name)

	# Prepare input text
	ai_text = "Your AI-generated text here..."
	prompt = f"""Please paraphrase the following text to make it more human-like while preserving the original meaning:

	{ai_text}

	Paraphrased text:"""

	# Generate paraphrased text
	inputs = tokenizer(prompt, return_tensors="pt")
	outputs = model.generate(
	inputs.input_ids,
	max_new_tokens=512,
	temperature=0.7,
	top_p=0.9,
	do_sample=True
	)
	paraphrased_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
	print(paraphrased_text.split("Paraphrased text:")[1].strip())
	```

	## Ethical Considerations

	AuthorMist Originality is released for research purposes to advance understanding of AI text detection limitations and privacy-preserving technologies. We acknowledge the dual-use nature of this technology and emphasize the following ethical considerations:

	1. Academic Integrity: This model should not be used to misrepresent AI-generated content as human-written in academic settings where such distinctions are ethically relevant.

	2. Transparency: We encourage users to maintain transparency about the use of AI assistance in content creation, even when using privacy-enhancing tools like AuthorMist.

	3. Privacy Protection: The primary legitimate use case for this technology is protecting author privacy and preventing unfair discrimination against AI-assisted writing in contexts where such assistance is permissible.

	4. Research Value: This model provides valuable insights into the limitations of current AI detection systems and contributes to the ongoing research dialogue about AI text detection and privacy.

	## Citation

	If you use AuthorMist Originality in your research, please cite our paper:

	```bibtex
	@article{authormist2025,
	title={AuthorMist: Evading AI Text Detectors with Reinforcement Learning},
	author={David, Isaac and Gervais, Arthur},
	journal={arXiv preprint},
	year={2025}
	}
	```

	## License

	This model is released under the [MIT License](https://opensource.org/licenses/MIT).

	## Acknowledgments

	We thank the developers of Qwen2.5 for the base model and the creators of the CheckGPT dataset for providing valuable training data.