Open Source Large Language Models

Introduction

Large Language Models (LLMs) are AI systems that model and process human language[1]. They are called “large” because they have hundreds of millions or even billions of parameters pre-trained using a massive corpus of text data[1]. LLMs are the foundation models of popular and widely used chatbots[1]. However, a parallel movement in the LLM space is rapidly gaining pace: open-source LLMs[1].

Proprietary vs Open-Source LLMs

Proprietary LLMs, such as GPT-4 and Google’s PaLM 2, are owned by a company and can only be used by customers after buying a license[1]. This license comes with rights, possible restrictions on using the LLM, and limited information on the mechanisms behind the technology[1].

On the other hand, open-source LLMs are free and available for anyone to access, use for any purpose, modify, and distribute[2]. The term “open source” refers to the LLM code and underlying architecture being accessible to the public, meaning developers and researchers can use, improve, or modify the model[2].

Benefits of Open-Source LLMs

There are multiple short-term and long-term benefits to choosing open-source LLMs instead of proprietary LLMs[1]:

1. Enhanced data security and privacy: One of the biggest concerns of using proprietary LLMs is the risk of data leaks or unauthorized access to sensitive data by the LLM provider[1]. By using open-source LLM, companies will be solely responsible for protecting personal data, as they will completely control it[1].
2. Cost savings and reduced vendor dependency: Most proprietary LLMs require a license to use them[1]. This differs from open-source LLMs, which are usually free [1].

Popular Open-Source LLMs

The open-source community has already achieved significant milestones, with many open-source LLMs available for different purposes[1]. Some of the top open-source LLMs for 2024 include LLaMA 2, BLOOM, BERT and Mistral7B[4-6]. These models are all licensed for commercial use[3].

Case study

I developed two notebooks covering Mistral 7B [7] and Mixtral_8x7B[8] LLM in Google Colab.?

Conclusion

Open-source LLMs promise to make the rapidly growing field of LMMs and generative AI more accessible, transparent, and innovative[1]. They offer enhanced data security, privacy, cost savings, and reduced vendor dependency[1]. With the rise of open-source LLMs, the future of generative AI looks promising and exciting.

References

1.-?8 Top Open-Source LLMs for 2024 and Their Uses | DataCamp

2.-?Open source large language models: Benefits, risks and types - IBM Blog

3.-?GitHub - eugeneyan/open-llms: ?? A list of open LLMs available for commercial use.

4.-?Mistral’s Open Source LLM | Internet Public Library ( ipl.org )

5.-?Mistral 7B: An Open-Source LLM Pushing the Frontiers of AI - Lusera ( luseratech.com )

6.-?GitHub - mistralai/mistral-src: Reference implementation of Mistral AI 7B v0.1 model.

7.-?MLxDL/Mistral-7B-Instruct.ipynb at main · frank-morales2020/MLxDL · GitHub

8.-?MLxDL/Mixtral_8x7B.ipynb at main · frank-morales2020/MLxDL · GitHub