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
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216ce1c | 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 37 38 39 40 41 42 43 | {
"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
}
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