metadata

license: cc-by-nc-4.0
tags:
  - generated_from_trainer
  - instruction fine-tuning
model-index:
  - name: flan-t5-small-distil-v2
    results: []
language:
  - en
pipeline_tag: text2text-generation
widget:
  - text: how can I become more healthy?
    example_title: example

LaMini-FLAN-T5-783M

This model is one of our LaMini model series in paper "LaMini: A Diverse Herd of Distilled Models from Large-Scale Instructions". This model is a fine-tuned version of google/flan-t5-large on LaMini dataset that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our project repository.
You can view other LaMini model series as follow. Note that not all models are performing as well. Models with ✩ are those with the best overall performance given their size/architecture. More details can be seen in our paper.

Base model	LaMini series (#parameters)
T5	LaMini-T5-61M	LaMini-T5-223M	LaMini-T5-738M
Flan-T5	LaMini-Flan-T5-77M✩	LaMini-Flan-T5-248M✩	LaMini-Flan-T5-783M✩
Cerebras-GPT	LaMini-Cerebras-111M	LaMini-Cerebras-256M	LaMini-Cerebras-590M	LaMini-Cerebras-1.3B
GPT-2	LaMini-GPT-124M✩	LaMini-GPT-774M✩	LaMini-GPT-1.5B✩
GPT-Neo	LaMini-Neo-125M	LaMini-Neo-1.3B
GPT-J	coming soon
LLaMA	coming soon

Use

Intended use

We recommend using the model to response to human instructions written in natural language.

We now show you how to load and use our model using HuggingFace pipline().

# pip install -q transformers
from transformers import pipeline

checkpoint = "{model_name}"

model = pipeline('text2text-generation', model=checkpoint, use_auth_token=True, device=0)

input_prompt = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
generated_text = generator(input_prompt, max_length=512, do_sample=True)[0]['generated_text']

print("Response": generated_text)

Training Procedure

We initialize with google/flan-t5-large and fine-tune it on our LaMini dataset. Its total number of parameters is 77M.

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0005
train_batch_size: 128
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 512
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Evaluation

We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our paper.

Limitations

More information needed

Citation

@misc{lamini,
      title={LaMini: A Diverse Herd of Distilled Models from Large-Scale Instructions}, 
      author={},
      year={2023},
      publisher = {GitHub},
      journal = {GitHub repository},
}