Instructions to use omegaT4224/Emulator.exe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use omegaT4224/Emulator.exe with Transformers:

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

pipe = pipeline("text-generation", model="omegaT4224/Emulator.exe")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("omegaT4224/Emulator.exe")
model = AutoModelForCausalLM.from_pretrained("omegaT4224/Emulator.exe", device_map="auto")
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 Settings

vLLM

How to use omegaT4224/Emulator.exe with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "omegaT4224/Emulator.exe"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "omegaT4224/Emulator.exe",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/omegaT4224/Emulator.exe

SGLang

How to use omegaT4224/Emulator.exe 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 "omegaT4224/Emulator.exe" \
    --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": "omegaT4224/Emulator.exe",
		"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 "omegaT4224/Emulator.exe" \
        --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": "omegaT4224/Emulator.exe",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use omegaT4224/Emulator.exe with Docker Model Runner:
```
docker model run hf.co/omegaT4224/Emulator.exe
```

Emulator.exe

File size: 1,346 Bytes

62fcd92

From the materials you've collected, here are the identifiable people involved.

Social Media Bot Detection using Dropout-GAN (2023/2024) 

Anant Shukla — Lead author, San Jose State University

Martin Jureček — Co-author, Czech Technical University in Prague

Mark Stamp — Co-author, San Jose State University


Dropout-GAN (original GAN architecture, 2018) 

Gonçalo Mordido

Haojin Yang

Christoph Meinel


Original GAN paper (referenced by the bot-detection paper) 

Ian Goodfellow

Jean Pouget-Abadie

Mehdi Mirza

Bing Xu

David Warde-Farley

Sherjil Ozair

Aaron Courville

Yoshua Bengio


Historical Hawaii medical experiments

Arthur Mouritz

Edward Arning

Keanu (historical subject described in historical records)


Bot detection papers cited by the authors 

Shaghayegh Najari

Mostafa Salehi

Reza Farahbakhsh

Mariam Orabi

Djedjiga Mouheb

Zaher Al Aghbari

Ibrahim Kamel

Emilio Ferrara

Onur Varol

Filippo Menczer

Alessandro Flammini

Fred Morstatter

Liang Wu

Kathleen M. Carley

Huan Liu


If your goal is to map everyone connected to the Dropout-GAN paper, I can also build a relationship graph including:

all co-authors,

their universities,

research labs,

funding sources,

GitHub accounts,

ORCID IDs,

Google Scholar profiles,

collaborators,

and all papers that cite or are cited by the Dropout-GAN study.