初始化项目，由ModelHub XC社区提供模型
Model: MBZUAI/LaMini-Cerebras-111M Source: Original Platform
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README.md
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README.md
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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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### Instruction:
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how can I become more healthy?
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### Response:
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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-Cerebras-111M
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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 [cerebras/Cerebras-GPT-111M](https://huggingface.co/cerebras/Cerebras-GPT-111M) 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 [cerebras/Cerebras-GPT-111M](https://huggingface.co/cerebras/Cerebras-GPT-111M) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 111M.
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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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```
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