Lamini Flan T5 248M

🚀 LaMini-Flan-T5-248M

LaMini-Flan-T5-248M is a fine - tuned model from the LaMini - LM series, designed to handle natural language instructions effectively.

This model belongs to our LaMini - LM model series, as presented in the paper "LaMini - LM: A Diverse Herd of Distilled Models from Large - Scale Instructions". It is a fine - tuned version of [google/flan - t5 - base](https://huggingface.co/google/flan - t5 - base) on the [LaMini - instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini - instruction), which contains 2.58M samples for instruction fine - tuning. For more details about our dataset, please visit our project repository.

You can explore other models in the LaMini - LM series below. Models marked with ✩ offer the best overall performance given their size/architecture, so we recommend using them. More details are available in our paper.

🚀 Quick Start

Intended use

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

Usage Examples

Basic Usage

# pip install -q transformers
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)

📚 Documentation

Training Procedure

We initialize with [google/flan - t5 - base](https://huggingface.co/google/flan - t5 - base) and fine - tune it on our [LaMini - instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini - instruction). Its total number of parameters is 248M.

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

📄 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}
}