README.md

---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-generation
widget:
  - text: >-
      Below is an instruction that describes a task.
      
      Write a response that appropriately completes the request.

      
      
      ### Instruction:
      
      how can I become more healthy?
      
      
      ### Response:
    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-GPT-1.5B

[![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 [gpt2-xl](https://huggingface.co/gpt2-xl) 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 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()`. 

```python
# 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

<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 [gpt2-xl](https://huggingface.co/gpt2-xl) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 1.5B. 

### 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

```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}
}

```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MBZUAI__LaMini-GPT-1.5B)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 31.56   |
| ARC (25-shot)         | 31.4          |
| HellaSwag (10-shot)   | 48.38    |
| MMLU (5-shot)         | 29.92         |
| TruthfulQA (0-shot)   | 42.47   |
| Winogrande (5-shot)   | 55.88   |
| GSM8K (5-shot)        | 0.0        |
| DROP (3-shot)         | 12.85         |