---
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-T5-77M
[]()
This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)". This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/).
You can view other LaMini-LM 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.
## 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()`.
```python
# 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-small](https://huggingface.co/google/flan-t5-small) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). 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
```bibtex
@misc{lamini,
title={LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
author={Minghao Wu and Abdul Waheed and Chiyu Zhang and Muhammad Abdul-Mageed and Alham Fikri Aji},
year={2023},
publisher = {GitHub},
journal = {GitHub repository},
}
```