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MBZUAI / LaMini-GPT-774M
Model's Last Updated: April 28 2023
text-generation
Introduction of LaMini-GPT-774M
Model Details of LaMini-GPT-774M
LaMini-GPT-774M
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
gpt2-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 |
Use
Intended use
We recommend using the model to respond to human instructions written in natural language. Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance. See the example on the right or the code below.
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('text-generation', model = checkpoint)
instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
print("Response", generated_text)
Training Procedure
We initialize with gpt2-large and fine-tune it on our LaMini-instruction dataset . Its total number of parameters is 774M.
Training Hyperparameters
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
@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}
}
Runs of MBZUAI LaMini-GPT-774M on huggingface.co
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More Information About LaMini-GPT-774M huggingface.co Model
Url of LaMini-GPT-774M
LaMini-GPT-774M huggingface.co Url
Provider of LaMini-GPT-774M huggingface.co
Other API from MBZUAI
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