| Copied from https://huggingface.co/susnato/phi-2 commit@9070ddb4fce238899ddbd2aca1faf6a0aeb6e444. | |
| This model can be loaded using HuggingFace `transformers` [commit@4ab5fb8941a38d172b3883c152c34ae2a0b83a68](https://github.com/huggingface/transformers/tree/4ab5fb8941a38d172b3883c152c34ae2a0b83a68). | |
| Below is the original introduction, which may be expired now. | |
| ---------------------------------------------------- | |
| **DISCLAIMER**: I don't own the weights to this model, this is a property of Microsoft and taken from their official repository : [microsoft/phi-2](https://huggingface.co/microsoft/phi-2). | |
| The sole purpose of this repository is to use this model through the `transformers` API or to load and use the model using the HuggingFace `transformers` library. | |
| # Usage | |
| First make sure you have the latest version of the `transformers` installed. | |
| ``` | |
| pip install -U transformers | |
| ``` | |
| Then use the transformers library to load the model from the library itself | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model = AutoModelForCausalLM.from_pretrained("susnato/phi-2") | |
| tokenizer = AutoTokenizer.from_pretrained("susnato/phi-2") | |
| inputs = tokenizer('''def print_prime(n): | |
| """ | |
| Print all primes between 1 and n | |
| """''', return_tensors="pt", return_attention_mask=False) | |
| outputs = model.generate(**inputs, max_length=200) | |
| text = tokenizer.batch_decode(outputs)[0] | |
| print(text) | |
| ``` |