Hugging Face's logo

Aditya7864
/
LaMini-Flan-T5-248M

Text2Text Generation
Transformers
PyTorch
English
t5
Generated from Trainer
instruction fine-tuning
text-generation-inference
Inference Endpoints
Model card Files Community
LaMini-Flan-T5-248M
File size: 6,582 Bytes 
7516187
ffb2d16
 
 
 
 
 
 
 
 
 
 
 
 
 
7516187
ffb2d16
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
 
---

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-T5-248M

[![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-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/).  
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-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. 

### 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}

}

```