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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language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- wiola
- decoder-only
- causal-language-model
- pytorch
- transformers
- research
---
# Wiola-360M
Wiola-360M is a 360 million parameter decoder-only language model developed by OSCOWL AI.
## Architecture
Wiola introduces several architectural improvements over a standard Transformer decoder:
- Grouped Query Attention (GQA)
- Spiral Rotary Positional Encoding (SRPE)
- Adaptive Token Merging (ATM)
- Gated Cross Layer Attention (GCLA)
- Dual Stream Feed Forward (DSFF)
## Model
- Parameters: 360M
- Context Length: 2048
- Vocabulary Size: 32000
- Attention Heads: 16
- Key/Value Heads: 4
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained(
"oscowlai/Wiola360M",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"oscowlai/Wiola360M",
trust_remote_code=True,
)
```
## License
Apache-2.0 |