I will give more info but this is how to generate text with the model. 
You will need to install
```bash
pip install peft
```

To run in python
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftConfig, PeftModelForCausalLM

peft_model_id = 'GrantC/alpaca-opt-1.3b-lora'
BASE_MODEL = 'facebook/opt-1.3b'
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(BASE_MODEL)
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)

model = PeftModelForCausalLM.from_pretrained(model, peft_model_id, device_map="auto")

prompt = "Write a blog post about shaving cream:"
print(prompt)
inputs = tokenizer(prompt, return_tensors='pt')
output = model.generate(input_ids=inputs["input_ids"], do_sample= True, penalty_alpha=0.6, top_k=4, max_new_tokens=256)
outputs = tokenizer.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(outputs)
```
{}
region:us
TAGS
#region-us 

I will give more info but this is how to generate text with the model. 
You will need to install


To run in python

TAGS
#region-us 

TAGS
#region-us 
