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	---
	license: cc-by-nc-4.0
	tags:
	- generated_from_trainer
	- instruction fine-tuning
	model-index:
	- name: flan-t5-small-distil-v2
	results: []
	language:
	- en
	pipeline_tag: text2text-generation
	widget:
	- text: >-
	how can I become more healthy?
	example_title: example
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	<p align="center" width="100%">
	<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
	</p>

	# LaMini-Flan-T5-783M

	[![Model License](https://img.shields.io/badge/Model%20License-CC%20By%20NC%204.0-red.svg)]()

	This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)". This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) 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.

	<table>
	<thead>
	<tr>
	<th>Base model</th>
	<th colspan="4">LaMini-LM series (#parameters)</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td>T5</td>
	<td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td>
	<td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td>
	<td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td>
	<td></td>
	</tr>
	<tr>
	<td>Flan-T5</td>
	<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td>
	<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td>
	<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td>
	<td></td>
	</tr>
	<tr>
	<td>Cerebras-GPT</td>
	<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td>
	<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td>
	<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td>
	<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td>
	</tr>
	<tr>
	<td>GPT-2</td>
	<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td>
	<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td>
	<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td>
	<td></td>
	</tr>
	<tr>
	<td>GPT-Neo</td>
	<td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td>
	<td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td>
	<td></td>
	<td></td>
	</tr>
	<tr>
	<td>GPT-J</td>
	<td colspan="4">coming soon</td>
	</tr>
	<tr>
	<td>LLaMA</td>
	<td colspan="4">coming soon</td>
	</tr>


	</tbody>
	</table>


	## Use

	### 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()`.

	```python
	# 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

	<p align="center" width="100%">
	<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
	</p>

	We initialize with [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 783M.

	### 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


	# Citation

	```bibtex
	@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}
	}
	```