File size: 1,509 Bytes
279e76a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
phi-2 - bnb 8bits
- Model creator: https://huggingface.co/susnato/
- Original model: https://huggingface.co/susnato/phi-2/
Original model description:
---
license: mit
license_name: microsoft-research-license
license_link: LICENSE
---
**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 uninstall -y transformers && pip install git+https://github.com/huggingface/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)
```
|