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

Libraries

How to use Eemansleepdeprived/Humaneyes with Transformers:

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

pipe = pipeline("text-generation", model="Eemansleepdeprived/Humaneyes")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Eemansleepdeprived/Humaneyes")
model = AutoModelForSeq2SeqLM.from_pretrained("Eemansleepdeprived/Humaneyes")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Eemansleepdeprived/Humaneyes with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Eemansleepdeprived/Humaneyes"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Eemansleepdeprived/Humaneyes",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Eemansleepdeprived/Humaneyes

SGLang

How to use Eemansleepdeprived/Humaneyes 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 "Eemansleepdeprived/Humaneyes" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Eemansleepdeprived/Humaneyes",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Eemansleepdeprived/Humaneyes" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Eemansleepdeprived/Humaneyes",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Humaneyes

Model Description

Humaneyes is an advanced text transformation model designed to convert AI-generated text into more human-like content and provide robust defense against AI content detection trackers. The model leverages sophisticated natural language processing techniques to humanize machine-generated text, making it indistinguishable from human-written content.

Model Details

Developed by: Eemansleepdeprived
Model type: AI-to-Human Text Transformation
Primary Functionality:
- AI-generated text humanization
- AI tracker defense
Language(s): English
Base Architecture: Pegasus Transformer
Input format: AI-generated text
Output format: Humanized, natural-sounding text

Key Capabilities

Transforms AI-generated text to sound more natural and human-like
Defeats AI content detection algorithms
Preserves original semantic meaning
Maintains coherent paragraph structure
Introduces human-like linguistic variations

Intended Use Cases

Academic writing assistance
Content creation and disguising AI-generated content
Protecting writers from AI content detection systems
Enhancing AI-generated text for more authentic communication

Ethical Considerations

Intended for creative and protective purposes
Users should respect academic and professional integrity
Encourages responsible use of AI-generated content
Not designed to facilitate academic dishonesty

Technical Approach

Humanization Strategies

Natural language variation
Contextual rephrasing
Introducing human-like imperfections
Semantic preservation
Stylistic diversification

Anti-Detection Techniques

Defeating AI content trackers
Randomizing linguistic patterns
Simulating human writing nuances
Breaking predictable AI generation signatures

Performance Characteristics

High semantic similarity to original text
Reduced AI detection probability
Contextually appropriate transformations
Minimal loss of original meaning

Limitations

Performance may vary based on input text complexity
Not guaranteed to bypass all AI detection systems
Potential subtle semantic shifts
Effectiveness depends on input text characteristics

Usage Example

from transformers import PegasusTokenizer, PegasusForConditionalGeneration

tokenizer = PegasusTokenizer.from_pretrained('Eemansleepdeprived/Humaneyes')
model = PegasusForConditionalGeneration.from_pretrained('Eemansleepdeprived/Humaneyes')

ai_generated_text = "Your AI-generated text goes here."
inputs = tokenizer(ai_generated_text, return_tensors="pt")
outputs = model.generate(**inputs)
humanized_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

Contact and Collaboration

For inquiries, feedback, or collaboration opportunities, contact:

Email: eeman.majumder@gmail.com

License

Released under the MIT License

Disclaimer

Users are responsible for ethical use of the Humaneyes Text Humanizer. Respect academic and professional guidelines.

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Safetensors

Model size

0.6B params

Tensor type

F32

Model tree for Eemansleepdeprived/Humaneyes

Base model

tuner007/pegasus_paraphrase

Finetuned

(2)

this model

Eemansleepdeprived
/

Humaneyes