Large Language Models (LLMs)

Open Source Large Language Models (LLMs)

This blog post will explore open source LLMs that are commercially viable.

• Open-source LLMs are large language models that are available for anyone to use, modify and distribute.
• This means that businesses and developers can use these models without having to pay licensing fees or worry about usage restrictions.

There are a number of benefits to using open-source LLMs:

• Cost-effective. Open-source LLMs are typically free to use, which can save businesses a significant amount of money.
• Customisable. Businesses can modify open-source LLMs to meet their specific needs. This can be useful for tasks such as fine-tuning the model for a particular domain or adding new features.
• Transparent. The code for open-source LLMs is publicly available, which means that businesses can see how the model works and make sure that it is aligned with their values.

However, it's crucial to understand the limitations and ethical considerations associated with using LLMs.

A List of Open LLMs Available for Commercial Use and their applications

• T5 (2019): A text-to-text transformer model that can be used for various tasks, such as translation, summarisation, and question answering.
• RWKV 4 (2021): Based on a recurrent neural network (RNN) architecture, this model can handle infinite context length, making it suitable for long-form text generation.
• Bloom (2022): A multilingual LLM with 176 billion parameters trained on a massive dataset of text and code. Bloom can perform various language-related tasks and generate different creative text formats.
• ChatGLM (2023): A 6-billion parameter model designed specifically for chatbot applications. It is known for its ability to engage in human-like conversations.
• Dolly (2023): This model was the world's first truly open instruction-tuned LLM. It excels at following instructions and completing tasks as instructed.
• StableLM-Alpha (2023): A model from Stability AI trained on a large dataset of text and code. StableLM-Alpha is designed for stability and reliability in text generation.
• MPT-7B (2023): This model sets a new standard for open-source, commercially usable LLMs. It is known for its long context length (84k) and suitability for various applications.
• Falcon (2023): Trained on a massive web dataset, the Falcon model series includes models with different parameter sizes, suitable for a wide range of applications.
• LLaMA 2 (2023): Released by Meta, this model series offers fine-tuned chat models and includes models with parameter sizes ranging from 7 billion to 70 billion. LLaMA 2 is known for its performance and customisable nature.
• Mistral 7B (2023): A powerful 7-billion parameter model that utilises a sliding window approach to handle context lengths up to 16k. Mistral 7B is recognised for its efficiency and performance.
• SantaCoder (2023): Designed for code generation, SantaCoder can assist developers in writing and understanding code.
• CodeGen2 (2023): Another code-focused LLM trained on programming and natural language data, making it proficient in code understanding and generation tasks.
• StarCoder (2023): A state-of-the-art LLM trained on a large dataset of code designed to be a powerful coding assistant.
• Code Llama (2023): This code-specialised model from Meta is available in various sizes, catering to different computational resources and use cases. It can generate different types of code and assist with programming tasks.

Open-source LLMs offer many benefits for businesses and developers. They are cost-effective, customisable, and transparent. With the growing number of open-source LLMs available, businesses are increasingly adopting this technology.

If you are interested in learning more about open-source LLMs, I encourage you to check out the sources [1] mentioned in this blog post. You can find a wealth of information on the GitHub repository, 'open-llms.'

Reference: 

[1] https://github.com/eugeneyan/open-llms

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#OpenSource, #LLMs, #TechBlogs, #FreeAIModels