Text Generation
Transformers
Safetensors
English
zeusmm
multimodal
chat
vision
audio
retrieval
text-generation-inference
custom_code
Instructions to use Wonder-Griffin/ZeusMM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wonder-Griffin/ZeusMM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Wonder-Griffin/ZeusMM", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Wonder-Griffin/ZeusMM", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Wonder-Griffin/ZeusMM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Wonder-Griffin/ZeusMM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Wonder-Griffin/ZeusMM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Wonder-Griffin/ZeusMM
- SGLang
How to use Wonder-Griffin/ZeusMM 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 "Wonder-Griffin/ZeusMM" \ --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": "Wonder-Griffin/ZeusMM", "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 "Wonder-Griffin/ZeusMM" \ --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": "Wonder-Griffin/ZeusMM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Wonder-Griffin/ZeusMM with Docker Model Runner:
docker model run hf.co/Wonder-Griffin/ZeusMM
| { | |
| "_name_or_path": "Wonder-Griffin/ZeusMM", | |
| "architectures": [ | |
| "ZeusForCausalLM" | |
| ], | |
| "audio_latents": 32, | |
| "audio_model_name": null, | |
| "auto_map": { | |
| "AutoConfig": "Wonder-Griffin/ZeusMM--zeus_mm.ZeusMMConfig", | |
| "AutoModelForCausalLM": "Wonder-Griffin/ZeusMM--zeus_mm.ZeusForCausalLM" | |
| }, | |
| "d_audio": 768, | |
| "d_ff": 2048, | |
| "d_model": 512, | |
| "d_retrieval": 768, | |
| "d_vision": 768, | |
| "dropout": 0.1, | |
| "film_hidden": 1024, | |
| "image_latents": 32, | |
| "initializer_range": 0.02, | |
| "is_decoder": true, | |
| "model_type": "zeusmm", | |
| "n_heads": 8, | |
| "n_layers": 6, | |
| "num_experts": 4, | |
| "retr_latents": 64, | |
| "retrieval_model_name": null, | |
| "rope_role_scales": [ | |
| 0.95, | |
| 1.0, | |
| 1.05 | |
| ], | |
| "rope_theta": 10000.0, | |
| "router_hidden": 256, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.46.2", | |
| "vision_model_name": null, | |
| "vocab_size": 50267 | |
| } | |