README.md

---

base_model: segestic/Tinystories-gpt-0.1-3m
datasets:
- roneneldan/TinyStories
inference: true
language:
- en
library_name: transformers
model_creator: segestic
model_name: Tinystories-gpt-0.1-3m
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
---


# Tinystories-gpt-0.1-3m-GGUF

Quantized GGUF model files for [Tinystories-gpt-0.1-3m](https://huggingface.co/segestic/Tinystories-gpt-0.1-3m) from [segestic](https://huggingface.co/segestic)

## 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 orignally used in the paper (https://arxiv.org/abs/2305.07759). Output model is 15mb and has 3 million parameters. 



# ------ EXAMPLE USAGE 1 ---

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)







# ------ EXAMPLE USAGE 2 ------

## 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)