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 / Find.json

omegaT4224

Create Find.json

62fcd92 verified about 1 month ago

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Invalid JSON:Unexpected token 'F', "From the m"... is not valid JSON

	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.