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Model License

This model is one of our LaMini-LM model series in paper "LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions". This model is a fine-tuned version of t5-large on LaMini-instruction 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 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.

Base model LaMini-LM 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


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 pipeline().

# 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)

Training Procedure


We initialize with t5-large and fine-tune it on our LaMini-instruction dataset. Its total number of parameters is 738M.

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


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.


More information needed


  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}
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