%global _empty_manifest_terminate_build 0 Name: python-gpt3-simple-primer Version: 0.1.2 Release: 1 Summary: GPT-3 wrapper for Python License: MIT License URL: https://github.com/happilyeverafter95/gpt-3 Source0: https://mirrors.nju.edu.cn/pypi/web/packages/93/b7/47e32af4f928a355c2f87697ba5fd030939e1b2f9e795761c9fe93ee0db8/gpt3_simple_primer-0.1.2.tar.gz BuildArch: noarch Requires: python3-openai %description # gpt3-simple-primer Simple GPT-3 primer using `openai`. ## Background Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. For more information, visit https://openai.com/blog/openai-api/. The [OpenAI Python library](https://github.com/openai/openai-python) is the official Python wrapper for the OpenAI API. The purpose of this library is to simplify the priming process by providing easy to use methods for setting the instructions and adding examples. ## Priming Priming is the practice of providing an initial prompt to the language model to improve subsequent model predictions. GPT-3 generally does very well even with short instructions and a few examples of your intended use case. Examples are typically delimited based on input and output. For instance, GPT-3 can be used to predict food ingredients based on the following prompt: ``` Given the name of a food, list the ingredients used to make this meal. Food: apple pie Ingredients: apple, butter, flour, egg, cinnamon, crust, sugar Food: guacamole Ingredients: avocado, tomato, onion, lime, salt ``` ## Requirements You will need an API key from OpenAI to access GPT-3. ## Installation To install, run: ``` pip install gpt3-simple-primer ``` ## Usage `input_text` and `output_text` determines how input and output are delimited in the examples. The default is to use `Input` and `Output`. ``` from gpt3_simple_primer import GPT3Generator, set_api_key KEY = 'sk-xxxxx' # openai key set_api_key(KEY) generator = GPT3Generator(input_text='Food', output_text='Ingredients') generator.set_instructions('List the ingredients for this meal.') generator.add_example('apple pie', 'apple, butter, flour, egg, cinnamon, crust, sugar') generator.add_example('guacamole', 'avocado, tomato, onion, lime, salt') # Ingredients: cream, egg yolk, sugar, lime, key lime juice generator.generate(prompt='key lime pie', engine='davinci', max_tokens=20, temperature=0.5, top_p=1) ``` To see the prompt used for priming: ``` generator.get_prompt() ``` To remove an example from the prompt: ``` generator.remove_example('apple pie') ``` ## Examples The library includes examples of GPT-3 applications based off of specific prompts. ``` from gpt3_simple_primer import set_api_key from gpt3_simple_primer.examples import idiom_explainer KEY = 'sk-xxxxx' # openai key set_api_key(KEY) idiom_explainer.generate('hill to die on', max_tokens=15, engine='davinci') ``` %package -n python3-gpt3-simple-primer Summary: GPT-3 wrapper for Python Provides: python-gpt3-simple-primer BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-gpt3-simple-primer # gpt3-simple-primer Simple GPT-3 primer using `openai`. ## Background Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. For more information, visit https://openai.com/blog/openai-api/. The [OpenAI Python library](https://github.com/openai/openai-python) is the official Python wrapper for the OpenAI API. The purpose of this library is to simplify the priming process by providing easy to use methods for setting the instructions and adding examples. ## Priming Priming is the practice of providing an initial prompt to the language model to improve subsequent model predictions. GPT-3 generally does very well even with short instructions and a few examples of your intended use case. Examples are typically delimited based on input and output. For instance, GPT-3 can be used to predict food ingredients based on the following prompt: ``` Given the name of a food, list the ingredients used to make this meal. Food: apple pie Ingredients: apple, butter, flour, egg, cinnamon, crust, sugar Food: guacamole Ingredients: avocado, tomato, onion, lime, salt ``` ## Requirements You will need an API key from OpenAI to access GPT-3. ## Installation To install, run: ``` pip install gpt3-simple-primer ``` ## Usage `input_text` and `output_text` determines how input and output are delimited in the examples. The default is to use `Input` and `Output`. ``` from gpt3_simple_primer import GPT3Generator, set_api_key KEY = 'sk-xxxxx' # openai key set_api_key(KEY) generator = GPT3Generator(input_text='Food', output_text='Ingredients') generator.set_instructions('List the ingredients for this meal.') generator.add_example('apple pie', 'apple, butter, flour, egg, cinnamon, crust, sugar') generator.add_example('guacamole', 'avocado, tomato, onion, lime, salt') # Ingredients: cream, egg yolk, sugar, lime, key lime juice generator.generate(prompt='key lime pie', engine='davinci', max_tokens=20, temperature=0.5, top_p=1) ``` To see the prompt used for priming: ``` generator.get_prompt() ``` To remove an example from the prompt: ``` generator.remove_example('apple pie') ``` ## Examples The library includes examples of GPT-3 applications based off of specific prompts. ``` from gpt3_simple_primer import set_api_key from gpt3_simple_primer.examples import idiom_explainer KEY = 'sk-xxxxx' # openai key set_api_key(KEY) idiom_explainer.generate('hill to die on', max_tokens=15, engine='davinci') ``` %package help Summary: Development documents and examples for gpt3-simple-primer Provides: python3-gpt3-simple-primer-doc %description help # gpt3-simple-primer Simple GPT-3 primer using `openai`. ## Background Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. For more information, visit https://openai.com/blog/openai-api/. The [OpenAI Python library](https://github.com/openai/openai-python) is the official Python wrapper for the OpenAI API. The purpose of this library is to simplify the priming process by providing easy to use methods for setting the instructions and adding examples. ## Priming Priming is the practice of providing an initial prompt to the language model to improve subsequent model predictions. GPT-3 generally does very well even with short instructions and a few examples of your intended use case. Examples are typically delimited based on input and output. For instance, GPT-3 can be used to predict food ingredients based on the following prompt: ``` Given the name of a food, list the ingredients used to make this meal. Food: apple pie Ingredients: apple, butter, flour, egg, cinnamon, crust, sugar Food: guacamole Ingredients: avocado, tomato, onion, lime, salt ``` ## Requirements You will need an API key from OpenAI to access GPT-3. ## Installation To install, run: ``` pip install gpt3-simple-primer ``` ## Usage `input_text` and `output_text` determines how input and output are delimited in the examples. The default is to use `Input` and `Output`. ``` from gpt3_simple_primer import GPT3Generator, set_api_key KEY = 'sk-xxxxx' # openai key set_api_key(KEY) generator = GPT3Generator(input_text='Food', output_text='Ingredients') generator.set_instructions('List the ingredients for this meal.') generator.add_example('apple pie', 'apple, butter, flour, egg, cinnamon, crust, sugar') generator.add_example('guacamole', 'avocado, tomato, onion, lime, salt') # Ingredients: cream, egg yolk, sugar, lime, key lime juice generator.generate(prompt='key lime pie', engine='davinci', max_tokens=20, temperature=0.5, top_p=1) ``` To see the prompt used for priming: ``` generator.get_prompt() ``` To remove an example from the prompt: ``` generator.remove_example('apple pie') ``` ## Examples The library includes examples of GPT-3 applications based off of specific prompts. ``` from gpt3_simple_primer import set_api_key from gpt3_simple_primer.examples import idiom_explainer KEY = 'sk-xxxxx' # openai key set_api_key(KEY) idiom_explainer.generate('hill to die on', max_tokens=15, engine='davinci') ``` %prep %autosetup -n gpt3-simple-primer-0.1.2 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-gpt3-simple-primer -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 0.1.2-1 - Package Spec generated