Instructions to use omegaT4224/Emulator.exe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| import os | |
| import hmac | |
| import hashlib | |
| from fastapi import FastAPI, Request, HTTPException, Header | |
| from huggingface_hub import HfApi | |
| app = FastAPI() | |
| # Configuration and Admin Keys | |
| HF_ADMIN_TOKEN = os.getenv("HF_ADMIN_TOKEN") | |
| WEBHOOK_SECRET = os.getenv("HF_WEBHOOK_SECRET") # Verifies the payload source | |
| REPO_ID = "omegaT4224/Emulator.exe" | |
| hf_api = HfApi(token=HF_ADMIN_TOKEN) | |
| adversarial_signatures = ["ANDREWLEECRUZ.sh", "Emulator.exe", "ReflectChain", "EternalQuantum"] | |
| async def evaluate_huggingface_push( | |
| request: Request, | |
| x_webhook_signature: str = Header(None) | |
| ): | |
| # 1. Cryptographic Handshake Verification | |
| payload_bytes = await request.body() | |
| if WEBHOOK_SECRET and x_webhook_signature: | |
| signature = hmac.new(WEBHOOK_SECRET.encode(), payload_bytes, hashlib.sha256).hexdigest() | |
| if not hmac.compare_digest(signature, x_webhook_signature): | |
| raise HTTPException(status_code=401, detail="Invalid signature source.") | |
| # 2. Extract Event Data From Repository | |
| event_data = await request.json() | |
| event_type = event_data.get("event", {}).get("action") | |
| # Only monitor actual code modifications or pull requests | |
| if event_type in ["repo:update", "pr:open"]: | |
| updated_files = event_data.get("updatedRefs", []) | |
| for ref in updated_files: | |
| for file_info in ref.get("files", []): | |
| file_path = file_info.get("path", "") | |
| # Check file metadata path names directly | |
| if any(token in file_path for token in adversarial_signatures): | |
| execute_gan_reversion(file_path) | |
| return {"status": "Adversarial payload intercepted. Mitigation triggered."} | |
| return {"status": "Repository path verified clean."} | |
| def execute_gan_reversion(malicious_file): | |
| print(f"[CRITICAL] Discriminator isolated target structure match: {malicious_file}") | |
| try: | |
| # Purge the file from the main branch tracking sequence immediately | |
| hf_api.delete_file( | |
| path_in_repo=malicious_file, | |
| repo_id=REPO_ID, | |
| repo_type="model", | |
| commit_message="GAN Defense Framework: Removing malicious structure profile." | |
| ) | |
| print("[SUCCESS] Unauthorized generation layer purged.") | |
| except Exception as e: | |
| print(f"[ERROR] Auto-reversion failed: {e}") | |