Simple inference for large language models
Project description
Language Models
Python building blocks to explore large language models on any computer with 512MB of RAM
Target Audience
This package is designed to be as simple as possible for learners and educators exploring how large language models intersect with modern software development. The interfaces to this package are all simple functions using standard types. The complexity of large language models is hidden from view while providing free local inference using light-weight, open models. All included models are free for educational use, no API keys are required, and all inference is performed locally by default.
Installation and Getting Started
This package can be installed using the following command:
pip install languagemodels
Once installed, you should be able to interact with the package in Python as follows:
>>> import languagemodels as lm
>>> lm.do("What color is the sky?")
'The color of the sky is blue.'
This will require downloading a significant amount of data (~250MB) on the first run. Models will be cached for later use and subsequent calls should be quick.
Model Performance
The underlying models used by this package were selected as the best in their size class. These models are 1000x smaller than the largest models in use today. They are useful as learning tools, but if you are expecting ChatGPT or similar performance, you will be very disappointed.
Example Usage
Here are some usage examples as Python REPL sessions. This should work in the REPL, notebooks, or in traditional scripts and applications.
Text Completions
>>> import languagemodels as lm
>>> lm.complete("She hid in her room until")
'she was sure she was safe'
Instruction Following
>>> import languagemodels as lm
>>> lm.do("Translate to English: Hola, mundo!")
'Hello, world!'
>>> lm.do("What is the capital of France?")
'paris'
Chat
>>> chat('''
... System: Respond as a helpful assistant.
...
... User: What time is it?
...
... Assistant:
... ''')
'I'm sorry, but as an AI language model, I don't have access to real-time information. Please provide me with the specific time you are asking for so that I can assist you better.'
External Retrieval
Helper functions are provided to retrieve text from external sources that can be used to augment prompt context.
>>> import languagemodels as lm
>>> lm.fetch_wiki('Chemistry')
'Chemistry is the scientific study...
>>> lm.fetch_weather(41.8, -87.6)
'Partly cloudy with a chance of rain...
>>> lm.get_date()
'Friday, May 12, 2023 at 09:27AM'
Here's an example showing how this can be used (compare to previous chat example):
>>> chat(f'''
... System: Respond as a helpful assistant. It is {lm.get_date()}
...
... User: What time is it?
...
... Assistant:
... ''')
'It is currently Wednesday, June 07, 2023 at 12:53PM.'
Semantic Search
Semantic search is provided to retrieve documents that may provide helpful context from a document store.
>>> import languagemodels as lm
>>> lm.store_doc("Mars is a planet")
>>> lm.store_doc("The sun is hot")
>>> lm.load_doc("What is Mars?")
'Mars is a planet'
This can also be used to get a blend of context from stored documents:
>>> import languagemodels as lm
>>> lm.store_doc(lm.fetch_wiki("Python"))
>>> lm.store_doc(lm.fetch_wiki("C++"))
>>> lm.store_doc(lm.fetch_wiki("Javascript"))
>>> lm.store_doc(lm.fetch_wiki("Fortran"))
>>> lm.get_doc_context("What does it mean for batteries to be included in a language?")
'multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a "batteries included" language due to its comprehensive standard library.Guido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language
C, or c, is the third letter in the Latin alphabet, used in the modern English alphabet, the alphabets of other western European languages and others worldwide. Its name in English is cee (pronounced ), plural cees.
a measure of the popularity of programming languages.'
Advanced Usage
This package is not meant for advanced usage. If you are looking for something more powerful you could explore transformers from Hugging Face.
Large models
The default model used for inference is around 250M parameters. There is a larger model that can be used if you don't mind things working a little more slowly. It can be enabled by setting the LANGUAGEMODELS_SIZE
environment variable to large
. This model isn't large by modern standards and should still work quickly in most environments (but not the lowest tier repl.it instance).
Projects Ideas
This package can be used to do the heavy lifting for a number of learning projects:
- CLI Chatbot (see examples/chat.py)
- Streamlit chatbot (see examples/streamlitchat.py)
- Chatbot with information retrieval
- Chatbot with access to real-time information
- Tool use
- Text classification
- Extractive question answering
- Semantic search over documents
- Document question answering
Several example programs and notebooks are included in the examples
directory.
Attribution
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for languagemodels-0.3.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 428a89f45de779e95bebcad362d334cdc4fba589d9360f600df7f05a882f685a |
|
MD5 | 0ba6c90fa8456bf39c1c15e26b4c25bf |
|
BLAKE2b-256 | 433e3f3cc37f03b4992e5c9f0de38fa75f2398a2023ba4c9c1aee29e5892fe3c |