languagemodels - Improve efficiency in working with large language models using a low-memory Python package

Language Models Project

The Language Models project offers a versatile package that simplifies the use of large language models within Python applications. With the ability to efficiently run on systems with as little as 512MB of RAM, it aims to make language model deployment accessible to a wide range of users while prioritizing data privacy by performing all inferences locally.

Installation and Initial Setup

The installation process for Language Models is straightforward, requiring a single command:

pip install languagemodels

Upon installation, users can immediately begin exploring the package through Python's interactive shell or in various scripting environments. The package initially downloads approximately 250MB of data, which is then cached for quick future access.

Practical Examples of Usage

Language Models provides intuitive ways to leverage language model capabilities. Consider the following examples:

Instruction Following: Translate phrases or answer questions with simple queries.
```
>>> lm.do("Translate to English: Hola, mundo!")
'Hello, world!'
```

Choice-Based Answers: Limit responses to predefined options.

>>> lm.do("Is Mars larger than Saturn?", choices=["Yes", "No"])
'No'

Model Performance Tuning: Adjust memory settings to access more robust language models.

>>> lm.config["max_ram"] = "4gb"
>>> lm.do("If I have 7 apples then eat 5, how many apples do I have?")
'I have 2 apples left.'

Utilizing GPU Acceleration: When available, leverage an NVIDIA GPU for faster performance.
```
>>> lm.config["device"] = "auto"
```

Text Completion and Chat Features: Generate text continuations or carry out conversations as a virtual assistant.

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

>>> lm.chat("User: What time is it?")
'I'm sorry, but as an AI language model, I don't have access to real-time information.'

Advanced Capabilities

The package includes several advanced features to extend its utility:

Code Completion: Autocomplete snippets of Python code.

>>> lm.code("a = 2\nb = 5\n# Swap a and b\n")
'a, b = b, a'

External Data Retrieval: Fetch information from external sources like Wikipedia or current weather data.
```
>>> lm.get_wiki('Chemistry')
'Chemistry is the scientific study...'
```

Semantic Search: Perform document searches through semantic indexing.

>>> lm.get_doc_context("What does it mean for batteries to be included in a language?")

Performance and Models

By employing int8 quantization and the CTranslate2 backend, the Language Models package offers superior CPU inference performance compared to some existing frameworks. Users have access to sensible default models, which are incrementally enhanced in performance with increased memory allocation (max_ram).

Commercial Licensing and Use

While the Language Models package itself is licensed for commercial use, users must verify the model licenses to ensure compliance during commercial applications. This can be done using appropriate license filtering functions within the package.

Educational and Learning Opportunities

The Language Models package is designed to be a resourceful tool for educators and learners exploring the interaction between large language models and software development. It powers various educational projects, such as creating chatbots that handle real-time information retrieval, tool applications, text classification, semantic searches, and more.

For a comprehensive understanding, users are encouraged to explore the available examples and utilize the package for diverse language modeling experiments.