
This model was created using GPT-2 as a base, and fine-tuned upon a dataset of elementary school problems requiring logic and reasoning.
Requires Pytorch

How to use to infer text
```python

from transformers import AutoTokenizer, AutoModelForCasualLM
import torch

type = "gpt2-large"
tokenizer = AutoTokenizer.from_pretrained(type)
model = AutoModelForCausalLM.from_pretrained(type)

model_path = '../model.pt'

model = torch.load(model_path)

your_text = "A courier received 50 packages yesterday and twice as many today.  All of these should be delivered tomorrow. How many packages should be delivered tomorrow?"
encoded_text = self.tokenizer.encode(your_text, return_tensors='pt')
outputs = model.generate(encoded_text, max_length=64, do_sample=True, temperature=0.5, top_p=1)
outputs = [tokenizer.decode(output) for output in outputs]
```
{"inference": {"parameters": {"temperature": 0.5}}, "widget": {"text": "A courier received 50 packages yesterday and twice as many today.  All of these should be delivered tomorrow. How many packages should be delivered tomorrow?"}}
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TAGS
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This model was created using GPT-2 as a base, and fine-tuned upon a dataset of elementary school problems requiring logic and reasoning.
Requires Pytorch

How to use to infer text

TAGS
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passage: TAGS
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