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
File size: 998 Bytes
b7720f0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | import argparse
import json
from pathlib import Path
from jsonschema import Draft7Validator
def validate(schema_path: Path, json_path: Path) -> int:
schema = json.loads(schema_path.read_text(encoding="utf-8"))
payload = json.loads(json_path.read_text(encoding="utf-8"))
validator = Draft7Validator(schema)
errors = sorted(validator.iter_errors(payload), key=lambda e: list(e.path))
if not errors:
print("VALID")
return 0
print("INVALID")
for err in errors[:50]:
path = ".".join(str(p) for p in err.path)
print(f"- {path}: {err.message}")
return 1
def main() -> int:
parser = argparse.ArgumentParser(description="Validate a GravityLLM scene JSON against the Spatial9Scene schema.")
parser.add_argument("schema_path", type=Path)
parser.add_argument("json_path", type=Path)
args = parser.parse_args()
return validate(args.schema_path, args.json_path)
if __name__ == "__main__":
raise SystemExit(main())
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