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

MIT License