MBZUAI
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LaMini-GPT-774M

@@ -1,4 +1,5 @@
 ---
 language:
 - en
 pipeline_tag: text-generation
@@ -19,196 +20,132 @@ widget:
     example_title: example
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
-## Model Details
-### Model Description
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-## Uses
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-## How to Get Started with the Model
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 ---
+license: cc-by-nc-4.0
 language:
 - en
 pipeline_tag: text-generation
     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/main/images/LaMnin.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
+</p>
+# LaMini-GPT-774M
+[![Model License](https://img.shields.io/badge/Model%20License-CC%20By%20NC%204.0-red.svg)]()
+This model is one of our LaMini model series in paper "[LaMini: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini)".
+This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on [LaMini 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/).
+You can view other LaMini model series as follow. Note that not all models are performing as well. Models with ✩ are those with the best overall performance given their size/architecture. More details can be seen in our paper.
+<table>
+<thead>
+  <tr>
+    <th>Base model</th>
+    <th colspan="4">LaMini 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 `pipline()`.
+```python
+# pip install -q transformers
+from transformers import pipeline
+checkpoint = "{model_name}"
+model = pipeline('text-generation', model=checkpoint, use_auth_token=True)
+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 = generator(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/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
+</p>
+We initialize with [gpt2-large](https://huggingface.co/gpt2-large) and fine-tune it on our [LaMini dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 77M.
+### 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
+@misc{lamini,
+      title={LaMini: A Diverse Herd of Distilled Models from Large-Scale Instructions},
+      author={},
+      year={2023},
+      publisher = {GitHub},
+      journal = {GitHub repository},
+}
+```