| ---
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| license: cc-by-nc-4.0
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| tags:
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| - generated_from_trainer
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| - instruction fine-tuning
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| model-index:
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| - name: flan-t5-small-distil-v2
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| results: []
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| language:
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| - en
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| pipeline_tag: text2text-generation
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| widget:
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| - text: >-
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| how can I become more healthy?
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| example_title: example
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| ---
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|
|
| <!-- 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. -->
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|
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| <p align="center" width="100%">
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| <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>
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| </p>
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|
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| # LaMini-Flan-T5-248M
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|
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| []()
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| 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-base](https://huggingface.co/google/flan-t5-base) 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/).
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| 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.
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|
|
| <table>
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| <thead>
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| <tr>
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| <th>Base model</th>
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| <th colspan="4">LaMini-LM series (#parameters)</th>
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| </tr>
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| </thead>
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| <tbody>
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| <tr>
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| <td>T5</td>
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| <td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td>
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| <td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td>
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| <td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td>
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| <td></td>
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| </tr>
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| <tr>
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| <td>Flan-T5</td>
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| <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td>
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| <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td>
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| <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td>
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| <td></td>
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| </tr>
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| <tr>
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| <td>Cerebras-GPT</td>
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| <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td>
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| <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td>
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| <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td>
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| <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td>
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| </tr>
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| <tr>
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| <td>GPT-2</td>
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| <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td>
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| <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td>
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| <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td>
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| <td></td>
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| </tr>
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| <tr>
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| <td>GPT-Neo</td>
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| <td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td>
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| <td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td>
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| <td></td>
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| <td></td>
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| </tr>
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| <tr>
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| <td>GPT-J</td>
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| <td colspan="4">coming soon</td>
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| </tr>
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| <tr>
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| <td>LLaMA</td>
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| <td colspan="4">coming soon</td>
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| </tr>
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|
|
|
|
| </tbody>
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| </table>
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|
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|
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| ## Use
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|
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| ### Intended use
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| We recommend using the model to response to human instructions written in natural language.
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| We now show you how to load and use our model using HuggingFace `pipeline()`.
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|
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| ```python
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| # pip install -q transformers
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| from transformers import pipeline
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|
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| checkpoint = "{model_name}"
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| model = pipeline('text2text-generation', model = checkpoint)
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| input_prompt = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
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| generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
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| print("Response", generated_text)
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| ```
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|
|
| ## Training Procedure
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|
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| <p align="center" width="100%">
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| <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>
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| </p>
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|
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| 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.
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|
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| ### Training Hyperparameters
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| The following hyperparameters were used during training:
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| - learning_rate: 0.0005
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| - train_batch_size: 128
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| - eval_batch_size: 64
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| - seed: 42
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| - gradient_accumulation_steps: 4
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| - total_train_batch_size: 512
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| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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| - lr_scheduler_type: linear
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| - num_epochs: 5
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|
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| ## Evaluation
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| 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]().
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|
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| ## Limitations
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| More information needed
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|
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| # Citation
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|
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| ```bibtex
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| @article{lamini-lm,
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| author = {Minghao Wu and
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| Abdul Waheed and
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| Chiyu Zhang and
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| Muhammad Abdul-Mageed and
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| Alham Fikri Aji
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| },
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| title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
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| journal = {CoRR},
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| volume = {abs/2304.14402},
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| year = {2023},
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| url = {https://arxiv.org/abs/2304.14402},
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| eprinttype = {arXiv},
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| eprint = {2304.14402}
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| }
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| ``` |
