🚀 LaMini-T5-61M
LaMini-T5-61M is a fine - tuned model from the LaMini - LM series, designed to respond to natural - language instructions.
🚀 Quick Start
This model is one of our LaMini - LM series in paper "[LaMini - LM: A Diverse Herd of Distilled Models from Large - Scale Instructions](https://github.com/mbzuai - nlp/lamini - lm)". It's a fine - tuned version of [t5 - small](https://huggingface.co/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 models of LaMini - LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper.
✨ Features
- Instruction - based Response: Designed to respond to human instructions written in natural language.
💻 Usage Examples
Basic Usage
from transformers import pipeline
checkpoint = "{model_name}"
model = pipeline('text2text-generation', model = checkpoint)
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 = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
print("Response", generated_text)
🔧 Technical Details
Training Procedure
We initialize with [t5 - small](https://huggingface.co/t5 - small) and fine - tune it on our [LaMini - instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini - instruction). Its total number of parameters is 61M.
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
📚 Documentation
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.
📄 License
This model is licensed under CC By NC 4.0.

Citation
@article{lamini-lm,
author = {Minghao Wu and
Abdul Waheed and
Chiyu Zhang and
Muhammad Abdul-Mageed and
Alham Fikri Aji
},
title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
journal = {CoRR},
volume = {abs/2304.14402},
year = {2023},
url = {https://arxiv.org/abs/2304.14402},
eprinttype = {arXiv},
eprint = {2304.14402}
}