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
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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 36 37 38 39 40 41 42 43 44 45 46 47 48 | # coding=utf-8
"""Wiola: a small language model with SRPE, GCLA, ATM, DSFF and WiolaRMSNorm.
Importing this package registers Wiola with the HuggingFace Auto* classes so
that ``AutoModelForCausalLM.from_pretrained("oscowlai/wiola-360m")`` works once
weights are published.
"""
from .configuration_wiola import WiolaConfig
from .modeling_wiola import (
WiolaDecoderLayer,
WiolaForCausalLM,
WiolaModel,
WiolaPreTrainedModel,
)
__version__ = "0.1.1"
__all__ = [
"WiolaConfig",
"WiolaModel",
"WiolaForCausalLM",
"WiolaPreTrainedModel",
"WiolaDecoderLayer",
]
def _register_auto_classes():
try:
from transformers import AutoConfig, AutoModel, AutoModelForCausalLM
except Exception: # transformers not installed
return
try:
AutoConfig.register("wiola", WiolaConfig)
AutoModel.register(WiolaConfig, WiolaModel)
AutoModelForCausalLM.register(WiolaConfig, WiolaForCausalLM)
except Exception:
# Already registered (e.g. re-import) — safe to ignore.
pass
# The auto_map in config.json now handles Hub loading; we no longer call
# register_for_auto_class to avoid save_pretrained() packaging source code.
# WiolaConfig.register_for_auto_class("AutoConfig")
# WiolaModel.register_for_auto_class("AutoModel")
# WiolaForCausalLM.register_for_auto_class("AutoModelForCausalLM")
_register_auto_classes() |