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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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widget: |
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- text: >- |
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Below is an instruction that describes a task. |
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Write a response that appropriately completes the request. |
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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 |
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should probably proofread and complete it, then remove this comment. --> |
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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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# LaMini-GPT-1.5B |
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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)". |
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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/). |
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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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## Use |
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### Intended use |
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We recommend using the model to respond to human instructions written in natural language. |
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Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance. |
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See the example on the right or the code below. |
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We now show you how to load and use our model using HuggingFace `pipeline()`. |
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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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checkpoint = "{model_name}" |
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model = pipeline('text-generation', model = checkpoint) |
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instruction = '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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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:" |
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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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<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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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. |
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### Training Hyperparameters |
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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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## Limitations |
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More information needed |
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# Citation |
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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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``` |
