Update README.md (#1)

- Update README.md (d07bce9314981218de14c5dd679f573ed7698304)

README.md

@@ -1,53 +1,149 @@
 ---
-license: apache-2.0
+license: cc-by-nc-4.0
 tags:
 - generated_from_trainer
+- instruction fine-tuning
 model-index:
-- name: flan-t5-base-distil-v4
+- 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. -->
 
-# flan-t5-base-distil-v4
-
-This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
+<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-FLAN-T5-77M
+
+[![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 [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) 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 response to human instructions written in natural language. 
+
+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('text2text-generation', model=checkpoint, use_auth_token=True, device=0)
+
+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 = 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 [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) and fine-tune it on our [LaMini dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 77M. 
+
+### Training Hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 0.0005
-- train_batch_size: 8
-- eval_batch_size: 8
+- train_batch_size: 128
+- eval_batch_size: 64
 - seed: 42
-- gradient_accumulation_steps: 64
+- 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
 
-### Training results
+## 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
 
 
-### Framework versions
+# Citation
 
-- Transformers 4.27.4
-- Pytorch 1.13.1+cu117
-- Datasets 2.2.0
-- Tokenizers 0.13.2
+```bibtex
+@misc{lamini,
+      title={LaMini: A Diverse Herd of Distilled Models from Large-Scale Instructions}, 
+      author={},
+      year={2023},
+      publisher = {GitHub},
+      journal = {GitHub repository},
+}
+```