

# Model summary

Train flan-T5-large on alpaca dataset with LoRA

# training

* torch==2.0.0+cu117
* transformers==4.28.0.dev0
* 8 x V100 32G

# How to use

```python
import transformers
from peft import PeftModel

base_model = transformers.AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large")
peft_model = PeftModel.from_pretrained("zirui3/flan-t5-large-alpaca")

inputs = tokenizer("Any instruction that you like.", return_tensors="pt")
outputs = peft_model.generate(**inputs, max_length=128, do_sample=True)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True)
```
{}
region:us
TAGS
#region-us 



# Model summary

Train flan-T5-large on alpaca dataset with LoRA

# training

* torch==2.0.0+cu117
* transformers==4.28.0.dev0
* 8 x V100 32G

# How to use


# Model summary

Train flan-T5-large on alpaca dataset with LoRA # training

* torch==2.0.0+cu117
* transformers==4.28.0.dev0
* 8 x V100 32G # How to use
TAGS
#region-us 
 # Model summary

Train flan-T5-large on alpaca dataset with LoRA # training

* torch==2.0.0+cu117
* transformers==4.28.0.dev0
* 8 x V100 32G # How to use
TAGS
#region-us 
# Model summary

Train flan-T5-large on alpaca dataset with LoRA# training

* torch==2.0.0+cu117
* transformers==4.28.0.dev0
* 8 x V100 32G# How to use