--- 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 ---

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# 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-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-large](https://huggingface.co/gpt2-large) 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.
Base model LaMini-LM series (#parameters)
T5 LaMini-T5-61M LaMini-T5-223M LaMini-T5-738M
Flan-T5 LaMini-Flan-T5-77M LaMini-Flan-T5-248M LaMini-Flan-T5-783M
Cerebras-GPT LaMini-Cerebras-111M LaMini-Cerebras-256M LaMini-Cerebras-590M LaMini-Cerebras-1.3B
GPT-2 LaMini-GPT-124M LaMini-GPT-774M LaMini-GPT-1.5B
GPT-Neo LaMini-Neo-125M LaMini-Neo-1.3B
GPT-J coming soon
LLaMA coming soon
## 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

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We initialize with [gpt2-large](https://huggingface.co/gpt2-large) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 774M. ### 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} } ```