peft safetensors generated_from_trainer base_model:google/flan-t5-large license:apache-2.0 region:us
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: google/flan-t5-large
model-index:
- name: flan-t5-large-samsum-qlora
  results: []
---

# Model description

flan-t5-large-samsum-qlora is an LLM model adapter and a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the 
[samsum](https://huggingface.co/datasets/samsum) dataset containing dialoges. Parameter-efficient fine-tuning with QLoRA was employed to fine-tune the base model. 
The model is intended for generative summarization tasks and achieved the following scores on the test dataset:
- Rougue1: 49.249596%
- Rouge2: 23.513032%
- RougeL: 39.960812%
- RougeLsum: 39.968438%



## How to use

Load the model:

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

# Load the peft adapter model config
peft_model_id = 'MuntasirHossain/flan-t5-large-samsum-qlora' 
peft_config = PeftConfig.from_pretrained(peft_model_id)

# load the base model and tokenizer
base_model = AutoModelForSeq2SeqLM.from_pretrained(peft_config.base_model_name_or_path,  load_in_8bit=True,  device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path)

# Load the peft model
model = PeftModel.from_pretrained(base_model, peft_model_id, device_map="auto")
model.eval()
```

Example Inference:

``` python
# random sample text from the samsum test dataset
text = """
Emma: Hi, we're going with Peter to Amiens tomorrow.
Daniel: oh! Cool.
Emma: Wanna join?
Daniel: Sure, I'm fed up with Paris.
Emma: We're too. The noise, traffic etc. Would be nice to see some countrysides.
Daniel: I don't think Amiens is exactly countrysides though :P
Emma: Nope. Hahahah. But not a megalopolis either!
Daniel: Right! Let's do it!
Emma: But we should leave early. The days are shorter now.
Daniel: Yes, the stupid winter time.
Emma: Exactly!
Daniel: Where should we meet then?
Emma: Come to my place by 9am.
Daniel: oohhh. It means I have to get up before 7!
Emma: Yup. The early bird gets the worm (in Amiens).
Daniel: You sound like my grandmother.
Emma: HAHAHA. I'll even add: no parties tonight, no drinking dear Daniel
Daniel: I really hope Amiens is worth it!
"""

input = tokenizer(text, return_tensors="pt")
outputs = model.generate(input_ids=input["input_ids"].cuda(), max_new_tokens=40) 
print("Summary: ", tokenizer.decode(outputs[0], skip_special_tokens=True))

Summary:  Emma and Peter are going to Amiens tomorrow. Daniel will join them. They will meet at Emma's place by 9 am. They will not have any parties tonight.
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results



### Framework versions

- PEFT 0.8.2
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2