Language Models

Python building blocks to explore large language models on any computer with 512MB of RAM

This package makes using large language models in software as simple as possible. All inference is performed locally to keep your data private 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.

Example Usage

Here are some usage examples as Python REPL sessions. This should work in the REPL, notebooks, or in traditional scripts and applications.

Instruction Following

>>> import languagemodels as lm

>>> lm.do("Translate to English: Hola, mundo!")
'Hello, world!'

>>> lm.do("What is the capital of France?")
'Paris.'

Adjusting Model Performance

The base model should run quickly on any system with 512MB of memory, but this memory limit can be increased to select more powerful models that will consume more resources. Here's an example:

>>> import languagemodels as lm
>>> lm.do("If I have 7 apples then eat 5, how many apples do I have?")
'You have 8 apples.'
>>> lm.set_max_ram('4gb')
4.0
>>> lm.do("If I have 7 apples then eat 5, how many apples do I have?")
'I have 2 apples left.'

Text Completions

>>> import languagemodels as lm

>>> lm.complete("She hid in her room until")
'she was sure she was safe'

Chat

>>> lm.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.'

Code

A model tuned on Python code is included. It can be used to complete code snippets.

>>> import languagemodels as lm
>>> lm.code("""
... a = 2
... b = 5
...
... # Swap a and b
... """)
'a, b = b, a'

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.get_wiki('Chemistry')
'Chemistry is the scientific study...

>>> lm.get_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):

>>> lm.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(lm.get_wiki("Python"), "Python")
>>> lm.store_doc(lm.get_wiki("C language"), "C")
>>> lm.store_doc(lm.get_wiki("Javascript"), "Javascript")
>>> lm.get_doc_context("What does it mean for batteries to be included in a language?")
'From Python document: 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 and first released it in 1991 as Python 0.9.

From C document: It was designed to be compiled to provide low-level access to memory and language constructs that map efficiently to machine instructions, all with minimal runtime support.'

Full documentation

Speed

This package currently outperforms Hugging Face transformers for CPU inference thanks to int8 quantization and the CTranslate2 backend. The following table compares CPU inference performance on identical models using the best available quantization on a 20 question test set.

Backend	Inference Time	Memory Used
Hugging Face transformers	22s	1.77GB
This package	11s	0.34GB

Note that quantization does technically harm output quality slightly, but it should be negligible at this level.

Models

This package does allow direct model selection, but sensible defaults are preferred to allow the package to improve over time as stronger models become available. The models used are 1000x smaller than the largest models in use today. They are useful as learning tools, but perform far below the current state of the art.

This package currently uses LaMini-Flan-T5-base as its default model. This is a fine-tuning of the T5 base model on top of the FLAN fine-tuning. The model is tuned to respond to instructions in a human-like manner. The following human evaluations were reported in the paper associated with this model family:

For code completions, the CodeT5+ series of models are used.

Commercial Use

This package itself is licensed for commercial use, but the models used may not be compatible with commercial use. In order to use this package commercially, you can filter models by license type using the require_model_license function.

>>> import languagemodels as lm
>>> lm.config['instruct_model']
'LaMini-Flan-T5-248M-ct2-int8'
>>> lm.require_model_license("apache|bsd|mit")
>>> lm.config['instruct_model']
'flan-t5-base-ct2-int8'

It is recommended to confirm that the models used meet the licensing requirements for your software.

Projects Ideas

One of the goals for this package is to be a straightforward tool for learners and educators exploring how large language models intersect with modern software development. It 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.