Top 5 Open-Source LLMs : Everything You Need To Know

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Top 5 Open Source LLMs You need to know [2024]

Summary:

Explore the forefront of AI innovation with the top 5 open-source Large Language Models (LLMs) of 2024. From Falcon’s groundbreaking 180B parameters to BLOOM’s multilingual prowess, delve into the cutting-edge features shaping the future. Discover the strengths and potential applications of Llama 2, GPT-NeoX-20B, and MPT-7B, empowering businesses to scale securely in the evolving AI landscape.

Introduction

The world of artificial intelligence (AI) is changing fast, and a big part of that change comes from something called Large Language Models (LLMs). These are not just regular tools; they’re like the leaders of a new phase in technology. Think of them as really smart systems that are changing the way we use our phones, computers, and other gadgets.

What is open source LLM?

An open source large language model (LLM) is a type of AI model that’s trained on a massive dataset of text and code, but unlike proprietary LLMs, its code and architecture are freely available for anyone to access, use, modify, and distribute. This transparency sets it apart from closed-source models, which are owned by companies and require licenses for use.

Enterprises may opt for open source LLM (Large Language Model) software instead of relying on external chatbot services like ChatGPT, Claude.ai, and Torri to address privacy and security concerns. Running an open source LLM on your machine ensures that sensitive data and confidential information remain within the enterprise’s control, minimizing the risk of exposure to external entities. This approach is particularly crucial on platforms where interactions might be reviewed by humans or used for training future models. By leveraging best open source LLM software locally, an enterprise can maintain a higher level of data security and confidentiality, addressing potential privacy issues associated with external applications.

What’s exciting is that many of these top LLMs are open-source. This means anyone with interest and some tech skills can use them, change them, and even improve them. It’s like having a super-smart AI friend that you can learn from and teach new tricks.

Top 5 best Open source LLMs of 2024

In this blog, we’re going to look at five of these best open source LLMs. Each one is special in its way, bringing new ideas and abilities to the world of AI.

Falcon 2 LLM

Falcon LLM stands as a groundbreaking open source large language model (LLM) developed by the Technology Innovation Institute (TII) in Abu Dhabi. It is designed to propel applications and use cases, ensuring the future resilience of our world. The suite currently encompasses the Falcon 180B, 40B, 7.5B, and 1.3B parameter AI models, along with the meticulously curated REFINEDWEB dataset. Together, they present a diverse and comprehensive array of solutions.

Here’s a comprehensive breakdown of its key features, strengths, and potential uses, along with relevant sources for further exploration:

Key Features:

Massive Size: With 180 billion parameters, Falcon 180B boasts an impressive capacity for learning and performance, surpassing several other open source LLMs.
Efficient Training: Trained on a refined dataset of 3.5 trillion tokens, ensuring accuracy and quality while optimizing resource usage.
Open-Source Availability: The code and training data are publicly available on Hugging Face, fostering transparency and community contributions.
Superior Performance: Falcon has outperformed GPT-3 on various benchmarks while requiring less training and inference resources, making it a more efficient option.
Diverse Models: TII offers various Falcon versions, including 180B, 40B, 7.5B, 1.3B parameter AI models, specialized models for specific tasks like long-form story writing.

Strengths:

High-quality data pipeline: TII’s rigorous data filtering and deduplication processes ensure accurate and reliable training data for Falcon.
Multilingual capabilities: Falcon can handle multiple languages effectively, though its primary focus is on English.
Fine-tuning potential: Falcon can be fine-tuned for specific tasks, further enhancing its performance and adaptability.
Community-driven development: The open-source nature allows for collaborative improvements and research, accelerating Falcon’s development.

Potential Applications:

Natural language processing (NLP): Falcon can excel in various NLP tasks like text summarization, sentiment analysis, and dialogue generation.
Creative content generation: The model can assist writers and artists in generating different creative formats like poems, scripts, and musical pieces.
Education and research: Personalized learning experiences, educational content generation, and research support are all potential applications.
Business and marketing: Falcon can power intelligent chatbots, personalize marketing campaigns, and analyze customer data effectively.

Additional Resources:

Falcon LLM website: https://www.tii.ae/news/abu-dhabi-based-technology-innovation-institute-introduces-falcon-llm-foundational-large
Hugging Face Falcon model card: https://huggingface.co/spaces/tiiuae/falcon-180b-demo
TII Falcon blog post: https://huggingface.co/tiiuae/falcon-180B
YouTube video on Falcon-180B: https://www.youtube.com/watch?v=9MArp9H2YCM

LLAMA 2

Llama 2, an open source large language model developed by Meta AI and Microsoft, showcases exceptional capabilities in generating diverse content, from poems to code, answering questions, and translating languages. It outperforms other LLMs in reasoning and coding benchmarks, emphasizing safety through reinforcement learning and providing a “Responsible Use Guide.” While still under development, users should be aware of potential inaccuracies, biased outputs, and the need for technical expertise for optimal use. Responsible utilization is paramount for unlocking the full potential of Llama 2 in revolutionizing various fields.

Built on the foundation of the original Llama, Llama 2 surpasses its predecessor in several ways:

Diverse Training: Trained on a much larger and varied dataset, ensuring better understanding and performance across different tasks.
Open Availability: Unlike the limited access of its predecessor, Llama 2 is readily available for research, development, and even commercial applications on platforms like AWS, Azure, and Hugging Face.
Safety Focus: Meta has prioritized safety by implementing measures to minimize misinformation, bias, and harmful outputs.
Enhanced Training: Offered in different versions with parameter counts ranging from 7 billion to 70 billion, catering to diverse needs and resources.

Llama 2 vs. Llama:

Here’s a quick comparison to understand the key differences:

Potential Applications of Llama 2:

Chatbots and Virtual Assistants: Improved dialogue capabilities can power more natural and engaging interactions.
Text Generation and Creative Content: Generate different creative formats like poems, scripts, or code, assisting writers and artists.
Code Generation and Programming: Help developers with tasks like code completion and bug detection.
Education and Research: Personalize learning experiences, generate educational content, and assist researchers with various tasks.
Business and Marketing: Enhance customer service through chatbots, personalize marketing campaigns, and analyze customer data.

Limitations and Considerations:

Like all LLMs, Llama 2 is still under development and can generate inaccurate or biased outputs.
Responsible and ethical use is crucial to avoid potential misuse and bias.
Different versions require varying computational resources, so choosing the right one is important.

Resources:

Meta AI LLAMA website: https://ai.meta.com/blog/large-language-model-llama-meta-ai/
Meta AI blog post on LLAMA2: https://ai.meta.com/blog/large-language-model-llama-meta-ai/
Hugging Face LLAMA2 model card: https://huggingface.co/models?search=llama

BLOOM LLM

Bloom LLM, born from the collaborative efforts of a global community, has become a true force in the open-source AI landscape. Here’s a comprehensive breakdown of its key features, potential applications, and what makes it unique:

What is BLOOM LLM?

BLOOM is a massive, multilingual LLM, boasting 176 billion parameters and trained on a staggering 46 languages and 13 programming languages. Developed through a year-long collaborative project involving Hugging Face and researchers from over 70 countries, BLOOM embodies the spirit of open-source AI.

Key Features of BLOOM:

Multilingual Prowess: Generate coherent and precise text in a whopping 46 languages, going beyond the typical English-centric models.
Open-Source Access: Both the source code and training data are publicly available, fostering transparency and community-driven improvement.
Autoregressive Text Generation: Extends and completes text sequences seamlessly, making it ideal for various creative and informative tasks.
Massive Parameter Count: With 176 billion parameters, BLOOM ranks among the most powerful open-source LLMs, offering superior performance.
Global Collaboration: The model’s development exemplifies the power of international cooperation in advancing AI technology.
Free Accessibility: Anyone can access and utilize BLOOM through the Hugging Face platform, democratizing access to cutting-edge AI tools.
Industrial-Scale Training: Trained on a vast amount of text data using significant computational resources, ensuring robust performance.

Potential Applications of BLOOM:

Multilingual Communication: Facilitate cross-cultural communication by translating text and generating language-specific content.
Creative Writing and Content Generation: Assist writers and artists in various formats like poems, scripts, code, musical pieces, etc.
Education and Research: Personalize learning experiences, generate educational materials, and support research endeavors across various fields.
Business and Marketing: Enhance customer service with multilingual chatbots, personalize marketing campaigns, and analyze data effectively.
Open-Source AI Development: Serve as a foundation for further research and development in open-source AI, fostering community innovation.

What makes BLOOM unique?

Multilingual Focus: Unlike many LLMs primarily focused on English, BLOOM’s multilingual capabilities open new possibilities for global communication and understanding.
Openness and Transparency: Public access to the code and training data allows for broader participation in improving and utilizing the model.
Collaborative Development: The model’s creation through global collaboration demonstrates the potential of open-source AI to bridge geographical and cultural barriers.

Limitations and Considerations:

As with all LLMs, BLOOM is still under development and can generate inaccurate or biased outputs. Responsible and ethical use is crucial.
Utilizing BLOOM effectively requires some technical knowledge and understanding of its capabilities.
The model’s large size might require significant computational resources for certain tasks.

Resources:

BigScience BLOOM website: https://huggingface.co/bigscience/bloom-intermediate
Hugging Face BLOOM model card: https://bigscience.huggingface.co/blog/bloom
BigScience blog post on BLOOM: https://huggingface.co/bigscience/bloom
BLOOM model card repository on GitHub: https://github.com/bigscience-workshop/model_card

GPT-NeoX-20B

It is another open-source LLM rising to prominence, showcases remarkable capabilities and potential. Here’s a breakdown of its key features, strengths, and potential applications:

What is GPT-NeoX-20B?

Developed by EleutherAI, GPT-NeoX-20B is a 20 billion parameter autoregressive language model trained on the Pile, a massive dataset of text and code.
Its architecture borrows from GPT-3 but with significant optimizations for improved performance and efficiency.
GPT-NeoX-20B excels in several areas:
- Few-shot reasoning: Performs exceptionally well on tasks requiring understanding and applying information from limited examples.
- Long-form text generation: Generates coherent and grammatically correct text even for lengthy sequences.
- Code generation and analysis: Can understand and generate code, assisting developers with various tasks.

Strengths of GPT-NeoX-20B:

Open-source: The model’s code and weights are publicly available, encouraging community contributions and research.
Efficient training: Utilizes the DeepSpeed library for efficient training, requiring less computational resources compared to other LLMs.
Strong few-shot learning: Performs exceptionally well on tasks with limited data, making it adaptable to diverse scenarios.
Long-form text generation: Generates coherent and grammatically correct text even for lengthy sequences, ideal for creative writing and content generation.
Code generation and analysis: Understands and generates code, potentially assisting developers with bug detection, code completion, and other tasks.

Potential Applications of GPT-NeoX-20B:

Personal assistants and chatbots: Enhance their capabilities in understanding and responding to complex questions and requests.
Creative writing and content generation: Assist writers and artists in generating different creative formats like poems, scripts, musical pieces, etc.
Education and research: Personalize learning experiences, generate educational content, and support research in various fields.
Software development: Assist developers with tasks like code completion, bug detection, and code analysis.
Open-source AI research: Serve as a foundation for further research and development in open-source AI, fostering innovation.

Limitations and Considerations:

As with all LLMs, GPT-NeoX-20B is still under development and can sometimes generate inaccurate or biased outputs. Responsible and ethical use is crucial.
Utilizing its full potential might require some technical knowledge and understanding of its capabilities.
The model’s size might require significant computational resources for certain tasks.

Resources:

EleutherAI GitHub repository: This is the official repository for GPT-NeoX-20B, where you can find the source code, training scripts, and pre-trained models. (Source: https://github.com/EleutherAI/gpt-neox)
Hugging Face model card: The Hugging Face model card provides a comprehensive overview of GPT-NeoX-20B, including its capabilities, limitations, and benchmark results. (Source: https://huggingface.co/EleutherAI/gpt-neox-20b)
EleutherAI blog post: This blog post by EleutherAI introduces GPT-NeoX-20B, discusses its architecture and training process, and highlights some of its potential applications. (Source: https://www.opensourceforu.com/2022/04/eleutherai-releases-gpt-neox-20b-a-20-billion-parameter-ai-language-model/)

MPT-7B

MPT-7B, short for MosaicML Pretrained Transformer, is a powerful open-source LLM developed by MosaicML Foundations. It boasts 7 billion parameters and is trained on a massive dataset of 1 trillion tokens, making it a capable competitor in the LLM landscape. Here’s a breakdown of its key features and potential applications, along with some relevant sources for further exploration:

Key Features:

Commercial Licensing: Unlike many open-source models, MPT-7B is licensed for commercial use, opening doors for businesses to leverage its capabilities.
Extensive Training Data: MPT-7B’s training on a diverse dataset of 1 trillion tokens ensures robust performance and adaptability across various tasks.
Long Input Handling: The model can handle exceptionally long inputs without compromising accuracy, making it ideal for tasks like summarizing lengthy documents.
Speed and Efficiency: Optimized for swift training and inference, MPT-7B delivers timely results, crucial for real-world applications.
Open-Source Code: The model’s efficient open-source training code promotes transparency and facilitates community contributions to its development.
Comparative Excellence: MPT-7B has demonstrated superior performance compared to other open-source models in the 7B-20B parameter range, even matching the quality of LLaMA-7B.

Potential Applications:

Predictive Analytics: MPT-7B can analyze large datasets to identify patterns and trends, informing business decisions and optimizing operations.
Decision-Making Support: The model can assist in complex decision-making processes by providing insights and recommendations based on analyzed data.
Content Generation and Summarization: MPT-7B can generate different creative text formats like poems, scripts, or code, or summarize long documents effectively.
Customer Service Chatbots: By understanding natural language and context, MPT-7B can power intelligent chatbots for improved customer service experiences.
Research and Development: The model can support research endeavors in various fields by analyzing data, generating hypotheses, and assisting with creative exploration.

Additional Resources:

MosaicML MPT-7B website: https://www.mosaicml.com/blog/mpt-7b
Hugging Face MPT-7B model card: https://huggingface.co/mosaicml/mpt-7b
MosaicML blog post on MPT-7B: https://www.mosaicml.com/blog/mpt-7b

Leverage best OpenSource LLMs with Creole Studios

Opensource Large Language Models (LLMs) are reshaping AI, offering flexibility and innovation for businesses. They’re great for creating new tech solutions and cutting development costs. However, challenges like data privacy and customization for specific business needs can be complex.

Creole Studios is your ideal partner in navigating these challenges. Our expertise in AI and machine learning means we can help your business harness the full potential of top LLMs efficiently and securely. We focus on creating tailor-made solutions that align with your unique goals, ensuring you stay ahead in the fast-evolving AI landscape.

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