Language Model Adapter Objects
A toolkit for adapting LLMs to perform downstream NLP tasks 🦾
lmao is an open-source library for integrating large language models (LLMs) from providers like OpenAI and Anthropic into your NLP workflows. For example, it can be used to pre-annotate text datasets using few-shot learning with Claude or GPT-4. lmao is in the (very) early stages of development. New features will be added with high cadence and documentation is coming soon.
The package is available on PyPI and can be installed with pip:
pip install lmao-nlpTo install the development version, clone the repository and install the package in editable mode with the dev extra:
git clone https://github.com/johnnygreco/lmao.git
cd lmao
pip install -e ".[dev]"If you want to install all the optional dependencies (e.g., to build the docs), use the all extra:
pip install -e ".[all]"The plan is to add support for all major LM providers (both for external API-based models and locally run models). Currently, the following providers are supported:
You'll need an activate API key from one or more of these providers to use lmao.
from lmao import factory
# create a chatbot using OpenAI's ChatGPT under the hood
chatbot = factory.create_chatbot("openai")
response = chatbot.chat("Hello! Tell me a joke, please!")
print(response.text)
# output: Why did the tomato turn red? Because it saw the salad dressing!
# the chatbot object keeps track of the chat history
response = chatbot.chat("Haha! Can you please explain why this is funny?")
print(response.text)
# output: This joke is funny because it plays on the idea of personification,
# where the tomato is given human-like characteristics, such as being able to
# see and react to its environment. It also sets up an expectation that there
# is a serious reason for the tomato turning red, but the punchline delivers
# a silly and unexpected twist, which makes it humorous. Finally, the wordplay
# on "dressing" which sounds similar to "stressin'" adds to the comedic effect.from lmao import factory
# create a sentiment analysis task using an adapter
# for Cohere's command-xlarge-nightly
model = factory.create_task("sentiment_analysis", "cohere")
# predict sentiment of the given text
result = model.predict("This lmao package is awesome!")
print(result.prediction)
# output: 'positive'from lmao import factory
# create a Fermi Task task using an adapter for Anthropic's Claude
fermi = factory.create_task("fermi_problem", "anthropic")
# ask a Fermi-Problem style question
response = fermi.ask("How many piano tuners are there in New York city?")
print(response.text)
# output:
# Here is a step by step approach to estimate this:
# 1) New York city population: Around 8.5 million people
# 2) Assume a typical piano tuning needs to be done once per year.
# Also assume 1 piano for every 100 people.
# 3) So number of pianos in NYC = 8.5 million * 1/100 = 85,000 pianos
# 4) Assume a piano tuner can tune around 2-3 pianos per day. So in a year
# (365 days) they can tune around 800-1200 pianos.
# 5) Number of piano tuners needed = Number of pianos / Number of pianos a
# tuner can tune in a year = 85,000 / 1000 = 85 piano tuners
# So my estimate is there are around 85 full-time piano tuners in
# New York City. Of course, there are assumptions and approximations in this
# estimate, but I think this approach gives a reasonable order of magnitude
# estimate using logical thinking and reasoning. Let me know if you would
# like me to clarify or expand on any part of the solution.