Text Generation
Transformers
Safetensors
PyTorch
English
wiola
decoder-only
causal-language-model
research
custom_code
Instructions to use oscowlai/Wiola360M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oscowlai/Wiola360M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="oscowlai/Wiola360M", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("oscowlai/Wiola360M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use oscowlai/Wiola360M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "oscowlai/Wiola360M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "oscowlai/Wiola360M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/oscowlai/Wiola360M
- SGLang
How to use oscowlai/Wiola360M 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 "oscowlai/Wiola360M" \ --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": "oscowlai/Wiola360M", "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 "oscowlai/Wiola360M" \ --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": "oscowlai/Wiola360M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use oscowlai/Wiola360M with Docker Model Runner:
docker model run hf.co/oscowlai/Wiola360M
File size: 921 Bytes
2db32a1 | 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 | {
"architectures": [
"WiolaForCausalLM"
],
"atm_enabled": true,
"atm_threshold": 0.92,
"bos_token_id": 1,
"dsff_narrow_size": 1024,
"dsff_wide_size": 4096,
"dtype": "float32",
"eos_token_id": 2,
"gcla_gate_init": -3.0,
"gcla_lookback": 2,
"hidden_size": 1024,
"initializer_range": 0.02,
"max_position_embeddings": 2048,
"model_type": "wiola",
"num_attention_heads": 16,
"num_hidden_layers": 16,
"num_key_value_heads": 4,
"pad_token_id": 0,
"rms_norm_eps": 1e-06,
"srpe_radial_amplitude": 0.1,
"srpe_radial_frequency": 0.01,
"srpe_spiral_divisor": 8,
"srpe_theta": 10000.0,
"transformers_version": "4.57.6",
"use_cache": true,
"vocab_size": 32000,
"auto_map": {
"AutoConfig": "configuration_wiola.WiolaConfig",
"AutoModel": "modeling_wiola.WiolaModel",
"AutoModelForCausalLM": "modeling_wiola.WiolaForCausalLM"
}
} |