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
| { | |
| "target_format": "iamf", | |
| "max_objects": 10, | |
| "style": "club", | |
| "section": "drop", | |
| "global": { | |
| "bpm": 128, | |
| "energy": 0.92 | |
| }, | |
| "stems": [ | |
| { | |
| "id": "v1", | |
| "class": "lead_vocal", | |
| "lufs": -16.8, | |
| "transient": 0.25, | |
| "band_energy": { | |
| "low": 0.1, | |
| "mid": 0.6, | |
| "high": 0.3 | |
| }, | |
| "leadness": 0.95 | |
| }, | |
| { | |
| "id": "k1", | |
| "class": "kick", | |
| "lufs": -10.5, | |
| "transient": 0.95, | |
| "band_energy": { | |
| "low": 0.8, | |
| "mid": 0.15, | |
| "high": 0.05 | |
| }, | |
| "leadness": 0.25 | |
| }, | |
| { | |
| "id": "b1", | |
| "class": "bass", | |
| "lufs": -12.2, | |
| "transient": 0.55, | |
| "band_energy": { | |
| "low": 0.85, | |
| "mid": 0.12, | |
| "high": 0.03 | |
| }, | |
| "leadness": 0.35 | |
| }, | |
| { | |
| "id": "p1", | |
| "class": "pad", | |
| "lufs": -20.3, | |
| "transient": 0.1, | |
| "band_energy": { | |
| "low": 0.2, | |
| "mid": 0.5, | |
| "high": 0.3 | |
| }, | |
| "leadness": 0.1 | |
| }, | |
| { | |
| "id": "s1", | |
| "class": "synth_lead", | |
| "lufs": -18.0, | |
| "transient": 0.4, | |
| "band_energy": { | |
| "low": 0.1, | |
| "mid": 0.55, | |
| "high": 0.35 | |
| }, | |
| "leadness": 0.75 | |
| }, | |
| { | |
| "id": "fx1", | |
| "class": "fx", | |
| "lufs": -23.0, | |
| "transient": 0.2, | |
| "band_energy": { | |
| "low": 0.1, | |
| "mid": 0.3, | |
| "high": 0.6 | |
| }, | |
| "leadness": 0.05 | |
| } | |
| ], | |
| "rules": [ | |
| { | |
| "type": "anchor", | |
| "track_class": "lead_vocal", | |
| "az_deg": 0, | |
| "el_deg": 10, | |
| "dist_m": 1.6 | |
| }, | |
| { | |
| "type": "mono_low_end", | |
| "hz_below": 120 | |
| }, | |
| { | |
| "type": "width_pref", | |
| "track_class": "pad", | |
| "min_width": 0.75 | |
| }, | |
| { | |
| "type": "avoid_band_masking", | |
| "mask_target": "lead_vocal", | |
| "band_hz": [ | |
| 1500, | |
| 4500 | |
| ] | |
| } | |
| ] | |
| } |