Text Generation
Transformers
Safetensors
English
quadorbit
custom-code
causal-lm
complex-valued
recurrent-attention
custom_code
Instructions to use Argo1-OOAS/QuadOrbit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Argo1-OOAS/QuadOrbit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Argo1-OOAS/QuadOrbit", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Argo1-OOAS/QuadOrbit", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Argo1-OOAS/QuadOrbit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Argo1-OOAS/QuadOrbit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Argo1-OOAS/QuadOrbit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Argo1-OOAS/QuadOrbit
- SGLang
How to use Argo1-OOAS/QuadOrbit 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 "Argo1-OOAS/QuadOrbit" \ --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": "Argo1-OOAS/QuadOrbit", "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 "Argo1-OOAS/QuadOrbit" \ --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": "Argo1-OOAS/QuadOrbit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Argo1-OOAS/QuadOrbit with Docker Model Runner:
docker model run hf.co/Argo1-OOAS/QuadOrbit
| { | |
| "architectures": [ | |
| "QuadOrbitForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_quadorbit.QuadOrbitConfig", | |
| "AutoModelForCausalLM": "modeling_quadorbit.QuadOrbitForCausalLM" | |
| }, | |
| "beta_init": 0.1, | |
| "beta_max": 0.5, | |
| "bos_token_id": 0, | |
| "d_ffn": 1505, | |
| "d_model": 512, | |
| "dtype": "float32", | |
| "dynamics_lr_scale": 0.25, | |
| "eos_token_id": 0, | |
| "eot_token_id": 0, | |
| "head_dim": 64, | |
| "init_std": 0.02, | |
| "max_position_embeddings": 512, | |
| "max_seq_len": 512, | |
| "model_type": "quadorbit", | |
| "n_heads": 8, | |
| "n_kv_heads": 1, | |
| "n_layers": 8, | |
| "native_gqa": false, | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 8, | |
| "num_key_value_heads": 1, | |
| "orbit_width": 8, | |
| "pad_token_id": 0, | |
| "qk_norm": true, | |
| "rms_eps": 1e-05, | |
| "rope_theta": 10000.0, | |
| "share_orbit_projections": true, | |
| "tie_embeddings": true, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.0.0", | |
| "use_cache": false, | |
| "variant": "stable_complex_orbit_attention_swiglu", | |
| "vocab_size": 32768 | |
| } | |