/sft_mathgpt2

Supervised Fine tuning using TRL library

Primary LanguageJupyter Notebook

MathGPT2

This model is a finetuned version of Sharathhebbar24/math_gpt2 using meta-math/MetaMathQA

Technical Details

  1. The model's repository is in HuggingFace Link: https://huggingface.co/Sharathhebbar24/math_gpt2_sft

  2. The model was trained on the following Specs

  • Kaggle Platform
  • T4
  • 29GB RAM

  1. It took around 47 mins to train the model for 3 epochs with a loss of 1.5.

  2. It was trained on FP16 bit architecture with a batch size of 2.

license datasets language
apache-2.0 meta-math/MetaMathQA en
  1. You can track the models graph here Graphs

Model description

GPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.

More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifting one token (word or piece of word) to the right. The model uses a masking mechanism to make sure the predictions for the token i only use the inputs from 1 to i but not the future tokens.

This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a prompt.

To use this model

>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> model_name = "Sharathhebbar24/math_gpt2_sft"
>>> model = AutoModelForCausalLM.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> def generate_text(prompt):
>>>  inputs = tokenizer.encode(prompt, return_tensors='pt')
>>>  outputs = model.generate(inputs, max_length=64, pad_token_id=tokenizer.eos_token_id)
>>>  generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
>>>  return generated[:generated.rfind(".")+1]
>>> prompt = "Gracie and Joe are choosing numbers on the complex plane. Joe chooses the point $1+2i$. Gracie chooses $-1+i$. How far apart are Gracie and Joe's points?"
>>> res = generate_text(prompt)
>>> res