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
google/flan-t5-base 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.
We recommend using the model to response to human instructions written in natural language.
1# pip install -q transformers
2from transformers import pipeline
3
4checkpoint = "{model_name}"
5
6model = pipeline('text2text-generation', model = checkpoint)
7
8input_prompt = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
9generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
10
11print("Response", generated_text)
We initialize with
google/flan-t5-base and fine-tune it on our
LaMini-instruction dataset. Its total number of parameters is 248M.
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.
