Out repository [flan-alpaca-lora](https://github.com/Reason-Wang/flan-alpaca-lora) contains the details to train flan-t5 with [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) instructions and [low-rank adaptation](https://arxiv.org/abs/2106.09685). 

You can use the following code easily.

Usage:

```python
import transformers
from peft import PeftModel

model_name = "google/flan-t5-large"; peft_model_id = "reasonwang/flan-alpaca-lora-large"
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
base_model = transformers.AutoModelForSeq2SeqLM.from_pretrained(model_name)
peft_model = PeftModel.from_pretrained(base_model, peft_model_id)

inputs = tokenizer("List a few tips to get good scores in math.", return_tensors="pt")
outputs = peft_model.generate(**inputs, max_length=128, do_sample=True)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))

```
{"license": "apache-2.0"}
arxiv:2106.09685 license:apache-2.0 region:us
TAGS
#arxiv-2106.09685 #license-apache-2.0 #region-us 

Out repository flan-alpaca-lora contains the details to train flan-t5 with Alpaca instructions and low-rank adaptation. 

You can use the following code easily.

Usage:


TAGS
#arxiv-2106.09685 #license-apache-2.0 #region-us 

TAGS
#arxiv-2106.09685 #license-apache-2.0 #region-us 
