---
license: apache-2.0
datasets:
- samsum
language:
- en
library_name: transformers
tags:
- peft
- lora
- t5
- flan
metrics:
- rouge
model-index:
- name: flan-t5-xxl-samsum-peft
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: train
      args: samsum
    metrics:
    - name: Rouge1
      type: rouge
      value: 50.386161
---

# FLAN-T5-XXL LoRA fine-tuned on `samsum`

PEFT tuned FLAN-T5 XXL model.


# flan-t5-base-samsum

This model is a fine-tuned version of [philschmid/flan-t5-xxl-sharded-fp16](https://huggingface.co/philschmid/flan-t5-xxl-sharded-fp16) on the samsum dataset.
It achieves the following results on the evaluation set:

- rogue1: 50.386161%
- rouge2: 24.842412%
- rougeL: 41.370130%
- rougeLsum: 41.394230%

- 

## How to use the model

The model was trained using 🤗 [PEFT](https://github.com/huggingface/peft). This repository only contains the fine-tuned adapter weights for LoRA and the configuration to load the model. Below you can find a snippet on how to run inference using the model. This will load the FLAN-T5-XXL from hugging face if not existing locally. 

1. load the model

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

# Load peft config for pre-trained checkpoint etc. 
peft_model_id = "philschmid/flan-t5-xxl-samsum-peft"
config = PeftConfig.from_pretrained(peft_model_id)

# load base LLM model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path,  load_in_8bit=True,  device_map={"":0})
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id, device_map={"":0})
model.eval()
```

2. generate
```python

text = "test"

input_ids = tokenizer(text, return_tensors="pt", truncation=True).input_ids.cuda()
outputs = model.generate(input_ids=input_ids, max_new_tokens=10, do_sample=True, top_p=0.9)
print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0])
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-3
- train_batch_size: auto
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.1
- PEFT@main