metadata

datasets:
  - roneneldan/TinyStories

Model trained on the TinyStories Dataset, see https://arxiv.org/abs/2305.07759

Based on GPT-Neo architecture.

License: mit

hyperparams used to train this model:

lr = 5e-4 lr_schedule = constant wd=0.1 adam_beta1=0.9, adam_beta2 = 0.95 context length=512 batch size=80 gradient accumulation steps=16

------ EXAMPLE USAGE ---

from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

model = AutoModelForCausalLM.from_pretrained('roneneldan/TinyStories-33M')

tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M")

prompt = "Once upon a time there was"

input_ids = tokenizer.encode(prompt, return_tensors="pt")

Generate completion

output = model.generate(input_ids, max_length = 1000, num_beams=1)

Decode the completion

output_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(output_text)