Tinystories Gpt 0.1 3m GGUF

🚀 Tinystories-gpt-0.1-3m-GGUF

This project provides quantized GGUF model files for the Tinystories-gpt-0.1-3m model created by segestic.

🚀 Quick Start

Model Information

Original Model Card

We tried to use the huggingface transformers library to recreate the TinyStories models on Consumer GPU using GPT2 Architecture instead of GPT - Neo Architecture originally used in the paper (https://arxiv.org/abs/2305.07759). Output model is 15mb and has 3 million parameters.

💻 Usage Examples

Basic Usage

# Use AutoTokenizer and AutoModelForCausalLM for text generation
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("segestic/Tinystories-gpt-0.1-3m")

model = AutoModelForCausalLM.from_pretrained("segestic/Tinystories-gpt-0.1-3m")

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)

Advanced Usage

# Use a pipeline as a high - level helper
from transformers import pipeline
# pipeline
pipe = pipeline("text-generation", model="segestic/Tinystories-gpt-0.1-3m")
# prompt
prompt = "where is the little girl"
# generate completion
output = pipe(prompt, max_length=1000, num_beams=1)
# decode the completion
generated_text = output[0]['generated_text']
# Print the generated text
print(generated_text)