The 5 Best Open Source Large Language Models in 2024

Large language models (LLMs) have rapidly become a cornerstone of modern artificial intelligence, revolutionizing natural language processing across diverse fields. From chatbots and virtual assistants to content generation and data analysis, LLMs are transforming how we interact with and leverage textual information.

Large language models are advanced AI systems trained on vast amounts of text data. They can understand, generate, and manipulate human-like text, making them powerful tools for a wide range of language-related tasks. These models learn patterns and relationships within language, enabling them to produce coherent and contextually relevant responses.

This article aims to guide you through the landscape of open-source large language models, helping you identify the best option for your specific project needs. We'll compare key features, performance metrics, and use cases to assist you in making an informed decision.

Open-source LLMS are those models that make their codes and training data publicly available. This openness allows a diversely skilled community of developers and researchers to use their code and improve the model further. It also increases trust in an LLM’s responses because the code is heavily tested before being available for public use.

OpenAI’s chief architect promises huge advancements in LLMs in the coming years. We can expect smarter and cheaper models with multimodality support. Here are a couple of benefits you might expect to see from these open-source LLMs:

• Customization: Open access to the code and weights allows you to customize models to specific needs and fine-tune them for particular applications.

• Cost efficiency: Working on existing open-source models reduces development costs and time spent building or upgrading models from scratch.

• Community support: The collaborative nature of open-source projects creates an actively available community to support and contribute to ongoing development.

• Innovation: They encourage experimentation among different teams, which creates room for rapid advancements and diverse applications through LLM’s code.

With new developments and benefits, there also might be some associated risks and drawbacks. You might encounter LLMs that: 

• Are hard to maintain: Open-source LLMs rely on community efforts for maintenance and updates so it becomes difficult to ensure their quality. 

• Pose security risks: Attackers can use the publicly available code to expose vulnerabilities.

• Require extra resources: Running and training LLMs requires substantial computational power and storage, which is not readily available to all users.

GPT-4 has become the most popular open-source LLM, there are also several other options that you can explore. Here is a list of 5 top open-source LLMs.

Llama 2 (Large Language Model Meta AI) is an advanced open-source large language model designed by Meta to perform several natural language processing tasks. It’s a collection of pre-trained and fine-tuned models with 7 billion to 70 billion parameters that can be optimized for multiple uses.

• Natural language understanding: Excels at comprehending complex language structures like intricate syntax and semantic nuances.

• Time tracking: Users can track the time spent on each task to identify time-consuming activities and improve time management.

• File management: Includes a secured file management system to organize, store, and access all task-related documents without needing multiple platforms.

• Reporting and analytics: Provides detailed reporting and analytics to get insights into productivity, identify areas for improvement, and support data-driven decisions.

Llama 2 provides extensive resources and community support to help users get started and effectively use the model. Their official documentation, which includes detailed guides on accessing and integrating the model, is available on Meta's platform.

The Llama 2 Community License grants users a non-transferable and royalty-free license to use, reproduce, distribute, and modify Llama 2 materials. However, organizations with over 700 million monthly active users must request a separate license from Meta.

BLOOM stands for BigScience Large Open-science Open-access Multilingual Language Model. It is a 176-billion-parameter LLM developed by the BigScience initiative and hosted by Hugging Face. It was designed with the collaboration of hundreds of researchers and engineers worldwide to generate text in 46 natural languages and 13 programming languages.

• Advanced tokenization: Uses a byte-level Byte Pair Encoding (BPE) tokenizer, which allows it to handle diverse languages and complex token sequences.

• Code generation: Help developers generate and debug code across 13 programming languages.

• Multilingual content generation: As BLOOM can generate content in 46 languages, it is ideal for global communications that can help businesses reach larger audiences.

• NLP research: Used as a tool by linguists and researchers for language studies, AI behavior analysis, and NLP research.

BLOOM has a strong and growing community that provides user support and comprehensive documentation. Hugging Face further improves this by providing detailed guides and examples to assist with any questions related to BLOOM.

It is released under the Responsible AI License (RAIL), which implies ethical AI use and promotes transparency. 

Open Pre-trained Transformer 175 Billion (OPT-175B) is a new LLM developed by Meta AI. Unlike many other LLMs, OPT-175B is openly available with its pre-trained models and training code to increase community engagement and collaborative research. 

OPT-175B was developed with energy efficiency in mind. That’s why its low carbon footprint was achieved using Meta’s Fully Sharded Data Parallel (FSDP) API and NVIDIA’s tensor parallelism techniques.

• Contextual understanding: Understands and generates context-sensitive responses with its unsupervised learning capabilities to autonomously adapt and create highly personalized content.

• Algorithmic innovations: Built with sparsification and quantization to reduce the memory footprint and accelerate its inference.

• Inference optimization: Supports high-throughput inference with offloading to GPUs and CPUs to balance memory usage and processing speed.

• Question-answering: Used in industries that require answering complex scientific questions during research in fields such as biology, physics, and chemistry.

OPT-175B’s detailed documentation contains the code to train and deploy the model. It also provides extensive notes and a logbook detailing the whole training process. This transparency makes sure that users understand the model's ins and outs.

OPT-175B is released under a non-commercial license to ensure it is used primarily for research purposes. To emphasize responsible AI practices, Meta AI has provided guidelines and resources on using this model with ethical and legal considerations.

MPT-7B is an open-source and commercially viable transformer-based language model developed by MosaicML. It has been trained from scratch on 1 trillion tokens of text and code using the MosaicML platform. A fun fact about this model is that it is trained with zero human intervention to increase its stability and efficiency.

• Suitable for long-form content: Handles up to 65k context lengths efficiently, which makes it ideal for tasks such as story writing and long-form document analysis.

• Fast processing: Uses advanced inference techniques like FlashAttention and FasterTransformer to ensure fast and efficient processing of tasks.

• Chatbots: Creates chatbots that can understand and engage in detailed conversations with users. 

• Custom Instruction Following: Fine-tunes the model for specific tasks or industries, such as legal document summarization or medical report generation.

MPT-7B’s community actively contributes to forums, discussion boards, and platforms like HuggingFace. In addition to this, MosaicML's LLM Foundry provides detailed resources for pretraining and fine-tuning MPT-7B for new and advanced users.

MPT-7B is licensed for commercial use, so businesses and regular users can modify and deploy the model in commercial applications without restrictions.

Vicuna 13B was developed by LMSYS and fine-tuned using the LLaMA and LLaMA 2 models. It's an open-source, high-quality chatbot that produces 90% ChatGPT-quality results to compete with leading models like OpenAI's ChatGPT and Google's Gemini. 

The project was a collaborative effort between UC Berkeley, CMU, Stanford, UC San Diego, and MBZUAI. Their team consisted of students and advisors who were experts in natural language processing and machine learning.

• Multi-turn questions: Provides challenging multi-turn and open-ended questions for evaluating chatbots.

• Intelligent chatbots: Creates interactive chatbots to answer complex questions and allow users to interact.

• Insights and analysis: Provides detailed analyses and insights based on large datasets or complex information.

• Interactive characters: Creates intelligent game characters that can engage players in meaningful conversations.

Vicuna-13B is available on Hugging Face, where users can participate in community discussions and share insights. The development team also interacts with users on Discord to provide real-time support. 

If you want to learn more about using Vicuna 13B, check out Datacamp resources. It has detailed tutorials on how to implement and use Vicuna-13B.

Vicuna-13B is primarily available for research and non-commercial use. Since it’s fine-tuned from the LLaMA model, it inherits the licensing terms of LLaMA. 

When selecting an open-source LLM, it's important to consider several factors to find the one that can work as your enterprise AI agent. Although open-source LLMs provide many advantages, the wide array of options available in the market can be overwhelming. That’s why data.world aims to make things easier for you.

Make sure to look for the following factors in your LLM:

• Performance and accuracy: Evaluate the model's ability to perform specific tasks, such as language understanding and problem-solving. 

• Model architecture: Different architectures, such as decoder-only transformers or a mixture of experts, have different strengths. So, consider the ability of the model’s architecture and its adaptability because it’s important for fine-tuning and customization. 

• Training data: Training data is a major factor that directly impacts the model's performance and accuracy. It depends on the quality and size of data to provide an LLM with the ability to generalize and perform well on required tasks.

• Scalability and efficiency: Scalability means efficiently handling the model's increasing data sizes and computational demands. That’s why you should choose efficient models, as they can reduce operational costs and improve performance in real-world scenarios.

• Ease of use: A model's integration capabilities, user interface, and accessibility level for developers and end-users determine user-friendliness. That’s why models that are easy to deploy and respond quickly to human language commands are preferred.

• Customization and flexibility: Choose the LLM that you can tailor to your organization’s specific needs. This way, it’ll be easier to fine-tune the model and modify it to suit particular tasks or domains better.

• Community and ecosystem: Active communities contribute to better documentation and faster updates, which helps maintain the model’s efficiency. That’s why the ecosystem around the LLM, including partnerships and integrations with other technologies, is essential to consider.

LLM responses are 300% more accurate than SQL databases when powered by a knowledge graph. That’s why the best way to improve an open-source model's accuracy is to use a data catalog platform built on knowledge graph architecture.

Here are some of the prime benefits of using a data catalog platform for your LLM:

Data catalogs create a centralized repository for discovering and accessing various data assets required for training and fine-tuning LLMs. This centralized access ensures that all relevant data is available in one place, so there are fewer chances of missing essential datasets. 

It also allows users to easily find and retrieve datasets which further speeds up the training process.

According to data.world's benchmark report, integrating LLMs with a knowledge graph can triple their response accuracy compared to using SQL databases alone. That’s why smart data catalogs use generative AI to improve productivity and speed up data preparation processes by automating administrative tasks and reducing manual intervention. 

Building an LLM on data.world’s knowledge graph technology provides critical business context and semantics between data. As a result, it allows LLMs to understand complex enterprise queries better and answer them accurately.

As open-source LLMs grow with more training data, organizations need a scalable and collaborative solution to manage their data assets. For this, they can use a suitable data catalog because it provides collaborative features for teams to discover and contribute to the training. 

Learn more about the power of Knowledge graphs in AI tools with data.world’s first AI Lab.

Knowledge graphs help LLMs give more accurate answers to complex business questions. data.world is one such data catalog, powered by a knowledge graph that helps you manage AI-ready data with context and semantics. Here’s how it empowers organizations to leverage the power of LLMs:

• Manage and organize data efficiently in a centralized repository where all data assets are readily available for LLM training and applications

• Build custom AI solutions that integrate seamlessly with LLMs for specific industry needs

• Provide automated data governance tools that ensure your data is protected and fed safely into your LLMs without vulnerabilities

Book a demo with data.world today and discover firsthand how our platform can help you create AI-ready data and enhance your LLM applications.