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 from 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)