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 the generated text
print(output_text)