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: 728 Bytes

69915be

import torch
from adapters import AutoAdapterModel
from transformers import AutoTokenizer

# 1. Initialize the correct base model for this specific adapter
base_model = "google/gemma-2-2b-it"
model = AutoAdapterModel.from_pretrained(base_model)

# 2. Load and activate the adapter weights
model.load_adapter(
    "omegaT4224/Andrewleecruz.vip",
    set_active=True
)

# 3. Load the matching tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model)

# 4. Format the prompt and tokenized inputs
inputs = tokenizer("Your custom prompt goes here", return_tensors="pt")

# 5. Generate and decode the response
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))