---
language: 
- en
- sp
- ja
- pe
- hi
- fr
- ch
- be
- gu
- ge
- te
- it
- ar
- po
- ta
- ma
- ma
- or
- pa
- po
- ur
- ga
- he
- ko
- ca
- th
- du
- in
- vi
- bu
- fi
- ce
- la
- tu
- ru
- cr
- sw
- yo
- ku
- bu
- ma
- cz
- fi
- so
- ta
- sw
- si
- ka
- zh
- ig
- xh
- ro
- ha
- es
- sl
- li
- gr
- ne
- as
- no

widget:
- text: "Translate to German:  My name is Arthur"
  example_title: "Translation"
- text: "Please answer to the following question. Who is going to be the next Ballon d'or?"
  example_title: "Question Answering"
- text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."
  example_title: "Logical reasoning"
- text: "Please answer the following question. What is the boiling point of Nitrogen?"
  example_title: "Scientific knowledge"
- text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?"
  example_title: "Yes/no question"
- text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"
  example_title: "Reasoning task"
- text: "Q: ( False or not False or False ) is? A: Let's think step by step"
  example_title: "Boolean Expressions"
- text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"
  example_title: "Math reasoning"
- text: "Premise:  At my age you will probably have learnt one lesson. Hypothesis:  It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?"
  example_title: "Premise and hypothesis"

tags:
- text2text-generation

datasets:
- svakulenk0/qrecc
- taskmaster2
- djaym7/wiki_dialog
- deepmind/code_contests
- lambada
- gsm8k
- aqua_rat
- esnli
- quasc
- qed
- financial_phrasebank


license: apache-2.0
---

# Model Card for LoRA-FLAN-T5 large

![model image](https://s3.amazonaws.com/moonup/production/uploads/1666363435475-62441d1d9fdefb55a0b7d12c.png)

This repository contains the LoRA (Low Rank Adapters) of `flan-t5-large` that has been fine-tuned on [`financial_phrasebank`](https://huggingface.co./datasets/financial_phrasebank) dataset.

## Usage

Use this adapter with `peft` library

```python
# pip install peft transformers
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

peft_model_id = "ybelkada/flan-t5-large-financial-phrasebank-lora"
config = PeftConfig.from_pretrained(peft_model_id)

model = AutoModelForSeq2SeqLM.from_pretrained(
    config.base_model_name_or_path, 
    torch_dtype='auto', 
    device_map='auto'
)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)
```

Enjoy!