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---
datasets:
- roneneldan/TinyStories
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
We tried to use the huggingface transformers library to recreate the TinyStories models on Consumer GPU. Output model has 9 million parameters.
Tweaked code of springtangent (https://github.com/springtangent/tinystoriestrainer/blob/main/tinystories_train.py)
Code credit - springtangent
------ EXAMPLE USAGE ---
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("segestic/Tinystories-0.1-9m")
model = AutoModelForCausalLM.from_pretrained("segestic/Tinystories-0.1-9m")
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)
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