Top 10 Open-Source LLM Models: Leading Large Language Models in AI

Discover the top 10 open-source large language models (LLMs) driving the future of generative AI. These transformer-based models, equipped with hundreds of millions to billions of parameters, excel in understanding and generating human language. Learn how these open-source LLMs are reshaping AI applications across industries.

Top 10 Open-Source LLM Models - Large Language Models

Large language models, or LLMs, are at the core of the current wave of advancements in generative AI. These models, based on transformers, represent AI systems capable of interpreting and generating language. They are called “large” because they come with hundreds of millions or even billions of pre-trained parameters sourced from vast text datasets.

In this article, we’ll explore the top 10 open-source LLMs available in 2024. While proprietary models like ChatGPT have been around for a short time, the open-source community has made significant strides. Here's a look at some of the most popular options!

Top 10 Open-Source LLM Models

  1. LLaMA 2
  2. BLOOM
  3. BERT (Bidirectional Encoder Representations from Transformers)
  4. Falcon 180B
  5. OPT-175B
  6. XGen-7B
  7. GPT-NeoX and GPT-J
  8. Vicuna 13-B
  9. YI 34B
  10. Mixtral 8x7B

1. LLaMA 2

Meta introduced LLaMA 2, a powerful open-source model with 7–70 billion parameters, in July 2023. It is designed for research and business purposes, offering flexibility and customization for natural language processing (NLP) tasks.

Syntax

char llamaExample = 'L';
Output

'L' is processed by LLaMA 2.

2. BLOOM

Developed by Flourish in 2022, BLOOM is an autoregressive LLM trained on a large multilingual dataset. With 176 billion parameters, it excels at generating fluent text in 46 languages and 13 programming languages.

Syntax

char bloomExample = 'B';
Output

'B' is processed by BLOOM in multiple languages.

3. BERT

BERT, developed by Google in 2017, was a breakthrough in the LLM space. Its bidirectional architecture allows it to excel in understanding text context and semantics.

Syntax

char bertExample = 'E';
Output

'E' is analyzed using BERT's bidirectional context.

4. Falcon 180B

The Falcon 180B model, released by the Technology Innovation Institute of UAE in 2023, boasts 180 billion parameters and a vast dataset of 3.5 trillion tokens. It is highly efficient in large NLP tasks.

Syntax

char falconExample = 'F';
Output

'F' is generated by Falcon 180B's NLP capabilities.

5. OPT-175B

Meta's OPT-175B model offers state-of-the-art NLP performance with 175 billion parameters. It is open-source but available under a non-commercial license, which restricts its use to research.

Syntax

char optExample = 'O';
Output

'O' is processed by OPT-175B's high efficiency.

6. XGen-7B

Salesforce launched XGen-7B in 2023, offering a context window of up to 8K tokens for extended input and output. Despite using only 7 billion parameters, XGen achieves high efficiency.

7. GPT-NeoX and GPT-J

GPT-NeoX and GPT-J are open-source LLMs developed by EleutherAI. GPT-NeoX has 20 billion parameters, while GPT-J has 6 billion, delivering strong performance in NLP tasks.

8. Vicuna 13-B

Vicuna 13-B is based on LLaMA 13B and refined using user-contributed conversations from ShareGPT. It is highly efficient for chatbot applications across multiple industries.

9. YI 34B

China's 01 AI developed YI 34B, which excels in generating English and Chinese text. With a context window of up to 32K tokens, it leads the Hugging Face leaderboard.

10. Mixtral 8x7B

Mixtral 8x7B, introduced by Mistral AI in 2023, uses a sparse mixture-of-experts network and has multilingual capabilities. It performs better than GPT 3.5 in many NLP tasks.

How to Choose the Right Open-Source LLM?

Choosing the right open-source LLM involves considering specific use cases, model capabilities, scalability, and community support. Define your requirements, evaluate model performance, and ensure that it meets your licensing and technical needs.