tiny-lm
This repository provides a tiny 16M parameters language model for debugging and testing purposes.
Trained on English and Japanese Wikipedia data.
How to use
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model = AutoModelForCausalLM.from_pretrained("sbintuitions/tiny-lm", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("sbintuitions/tiny-lm", use_fast=False)
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
print(generator("Hello", max_length=30, do_sample=True, top_k=100))
Model architecture
A 4-layer, 512-hidden-size transformer-based language model.
Training
The model was trained on English Wikipedia and Japanese Wikipedia to optimize a traditional language modelling objective for 25B tokens.
License
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Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "sbintuitions/tiny-lm"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sbintuitions/tiny-lm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'