Text Generation
Transformers
PEFT
English
gravityllm
spatial-audio
immersive-audio
spatial9
iamf
instruction-tuning
json
lora
qlora
Instructions to use Spatial9/GravityLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Spatial9/GravityLLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Spatial9/GravityLLM")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Spatial9/GravityLLM", dtype="auto") - PEFT
How to use Spatial9/GravityLLM with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Spatial9/GravityLLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Spatial9/GravityLLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Spatial9/GravityLLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Spatial9/GravityLLM
- SGLang
How to use Spatial9/GravityLLM 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 "Spatial9/GravityLLM" \ --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": "Spatial9/GravityLLM", "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 "Spatial9/GravityLLM" \ --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": "Spatial9/GravityLLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Spatial9/GravityLLM with Docker Model Runner:
docker model run hf.co/Spatial9/GravityLLM
| import argparse | |
| from pathlib import Path | |
| from huggingface_hub import HfApi | |
| def parse_args() -> argparse.Namespace: | |
| parser = argparse.ArgumentParser(description="Upload a local GravityLLM folder to the Hugging Face Hub.") | |
| parser.add_argument("--folder_path", type=Path, required=True, help="Local folder to upload.") | |
| parser.add_argument("--repo_id", type=str, required=True, help="Namespace/repo-name on Hugging Face.") | |
| parser.add_argument("--repo_type", type=str, default="model", choices=["model", "dataset", "space"]) | |
| parser.add_argument("--private", action="store_true") | |
| parser.add_argument("--commit_message", type=str, default="Upload GravityLLM artifacts") | |
| return parser.parse_args() | |
| def main() -> None: | |
| args = parse_args() | |
| api = HfApi() | |
| api.create_repo(repo_id=args.repo_id, repo_type=args.repo_type, private=args.private, exist_ok=True) | |
| api.upload_folder( | |
| folder_path=str(args.folder_path), | |
| repo_id=args.repo_id, | |
| repo_type=args.repo_type, | |
| commit_message=args.commit_message, | |
| ) | |
| print(f"Uploaded {args.folder_path} to https://huggingface.co/{args.repo_id}") | |
| if __name__ == "__main__": | |
| main() | |