| | --- |
| | license: apache-2.0 |
| | --- |
| | 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)) |
| | |
| | ``` |
