Key Highlights

• Open-source LLMs are gaining popularity and offer several benefits over proprietary models, including enhanced data security and privacy, cost savings, code transparency, and active community support.
• The top open-source LLMs for 2024 include Falcon 180B, LLaMA 2, BLOOM, GPT-NeoX and GPT-J, Vicuna 13-B, OPT-175B, XGen-7B, and so on.
• Evaluating open-source LLMs involves considering factors such as the open-source LLMs leaderboard, model size and computational efficiency, accuracy and language understanding, and customization and adaptability.
• Improving open-source LLMs can be done through fine-tuning techniques for better performance, leveraging cloud services for scalability, and implementing security measures for data protection.
• Challenges in using open-source LLMs include handling bias and ethical concerns, overcoming technical limitations, and ensuring continuous model improvement.
• FAQs: What makes an LLM “open source”? How can I contribute to the improvement of an open-source LLM? Can open-source LLMs rival proprietary models in performance? What are the upcoming trends in open-source LLMs for 2024?
• Conclusion: Open-source LLMs offer a promising alternative to proprietary models, with several top open-source LLMs available for different purposes. Evaluating and improving open-source LLMs can lead to enhanced performance and innovation in the field of generative AI.

Introduction

Open-source LLMs, or big language models, are popular for understanding human language. These AI systems use transformers and have millions or billions of parameters. They are trained with a lot of text data. Open-source LLMs have benefits like better security, cost savings, transparent code, and community support.

This blog will cover the best open-source LLMs in 2024 and how to assess and enhance them. We will look at each model’s features, strengths, and possible uses. We’ll also talk about the criteria for ranking these LLMs, such as their size, efficiency, accuracy, customization options.

By the end of this blog post, you will understand the top open-source LLMs in 2024 better. You’ll also learn about evaluation methods and ways to boost their performance. Let’s get started!

Benefits of Using Open-Source LLMs

Here are several compelling reasons why opting for open-source LLMs offers numerous short-term and long-term advantages compared to proprietary LLMs:

Enhanced data security and privacy

Improved data security and privacy concerns are among the primary reasons for favoring open-source LLMs over proprietary counterparts. Proprietary LLMs often raise apprehensions regarding potential data breaches or unauthorized access to sensitive information, as evidenced by past controversies surrounding the utilization of personal and confidential data for training purposes.

Adopting open-source LLMs places the responsibility for safeguarding personal data squarely on the companies themselves, granting them complete control over data protection measures.

Cost-effective and reduced vendor dependency

Moreover, embracing open-source LLMs can lead to significant cost savings and decreased dependence on vendors. Unlike proprietary LLMs, which typically necessitate licensing fees for usage, open-source alternatives are generally freely accessible.

Nevertheless, it's essential to acknowledge that deploying LLMs, regardless of their licensing model, demands substantial resources. This often entails expenses for utilizing cloud services or maintaining robust infrastructure, particularly for inference tasks.

transparency and customization

Open-source LLMs offer companies unparalleled access to the inner workings of language models, encompassing their source code, architecture, training data, and methodologies for training and inference. This transparency serves as a foundational element for both scrutiny and customization.

Given that open-source LLMs make their source code available to all, companies leveraging these models can tailor them to suit their specific requirements and use cases.

Active community support and fostering innovation

Furthermore, the open-source ethos fosters a vibrant community of support and encourages innovation. By democratizing access to LLM and generative AI technologies, the movement empowers developers worldwide to examine and enhance these models. This accessibility not only reduces barriers to entry but also facilitates collaboration, leading to advancements in model accuracy, performance, and the mitigation of biases.

Best Open Source LLMs for 2024

The field of open-source LLMs has seen significant advancements in recent years, with several top models available for different purposes. In this section, we will explore the best open-source LLMs for 2024 and highlight their unique features and capabilities. Each LLM offers its own strengths, making them suitable for a variety of tasks and use cases. Let’s delve into each of these top open-source LLMs and find their key features.

1. Falcon 180B

Falcon 180B is a powerful open-source LLM developed by the Technology Innovation Institute of the United Arab Emirates. With its impressive training on 180 billion parameters and 3.5 trillion tokens, Falcon 180B, also known as Falcon LLM, has swiftly ascended to the top of the LLM hierarchy. It has outperformed other LLMs in various natural language processing tasks and shows great potential in text generation. Falcon LLM, specifically Falcon-40B, is a foundational LLM equipped with 40 billion parameters and has been trained on an impressive one trillion tokens. It excels in tasks such as language understanding and text completion, making it a top choice for those looking to evaluate and improve their open source LLM.

As an open-source model, Falcon 180B offers transparency and access to its source code, allowing developers to customize and adapt it for their specific use cases. However, it’s important to note that Falcon 180B requires significant computing resources to function effectively in various tasks. Nonetheless, its impressive performance and open-source nature make it a promising choice for text generation and other natural language processing tasks. Falcon 180B contributes to the growing ecosystem of open-source LLMs, providing researchers and developers with more options and opportunities for innovation.

2. LLaMA 3

LLaMA 3, developed by Meta AI, is an open-source LLM that has gained attention for its impressive performance and versatility. With its 7 to 70 billion parameters, LLaMA 3 is a pre-trained generative text model that can be fine-tuned for a variety of natural language generation tasks, including programming tasks. It has been trained using reinforcement learning from human feedback, making it adaptive and capable of producing high-quality text through its app. However, it has been outperformed by Mistral AI’s Mistral 7B, which uses Sliding Window Attention (SWA) to optimize the model’s attention process and achieve significant speed improvements.

LLaMA 3 stands out in the open-source LLM space for its research and commercial use, making it suitable for both academic and industry applications. It offers an open-source license, allowing developers to access and customize the model according to their specific requirements. LLaMA 3 has already been used to develop customized versions such as Llama Chat and Code Llama, showcasing its ease of use and adaptability with Python integration. With its combination of performance, adaptability, and open-source nature, LLaMA 3 is a top choice for machine learning practitioners and researchers.

3. BLOOM

BLOOM, an open-source large language model, is a product of a collaborative project between researchers from Hugging Face and volunteers from 70+ countries. This autoregressive LLM is trained on vast amounts of text data using industrial-scale computational resources. With its impressive 176 billion parameters, BLOOM offers capabilities for coherent and accurate text generation in multiple languages and programming languages.

The key strength of BLOOM lies in its transparency and accessibility. The project is committed to providing open access to the source code and training data, enabling developers to study, run, and improve the model. BLOOM is available for free through the Hugging Face ecosystem, making it accessible to a wide range of users. Its open-source nature, combined with its impressive performance, positions BLOOM as a valuable tool for language generation tasks and contributes to the thriving open-source LLM community.

4. GPT-NeoX and GPT-J

GPT-NeoX and GPT-J are two notable open-source alternatives to the popular GPT series by OpenAI. Developed by researchers from EleutherAI, these LLMs offer impressive capabilities despite their relatively smaller parameter sizes. GPT-NeoX boasts 20 billion parameters, while GPT-J has 6 billion parameters.

Both models have been trained with high-quality datasets from diverse sources, enabling them to perform well in multiple domains and use cases. Although they have fewer parameters compared to other large LLMs, GPT-NeoX and GPT-J deliver results with high accuracy and can be used for various natural language processing tasks like text generation, sentiment analysis, and research. These open-source LLMs contribute to the democratization of generative AI technologies and provide developers with accessible tools for language processing and generation.

5. Vicuna 13-B

Vicuna 13-B is an open-source conversational model developed through fine-tuning the LLaMa 13B model. It utilizes user-shared conversations gathered from ShareGPT, providing a rich dataset for training and improving the model’s conversational abilities. Vicuna-13B is designed as an intelligent chatbot and offers applications across various industries, including customer service, healthcare, education, finance, and travel/hospitality. With a context length of 16k tokens, this model is capable of handling longer conversations and maintaining context over a more extended dialogue.

In preliminary evaluations, Vicuna-13B has shown impressive performance and outperformed other models like LLaMa and Alpaca in the majority of cases. It achieved more than 90% quality compared to ChatGPT and Google Bard, making it a promising choice for conversational AI applications. Vicuna-13B’s open-source nature and user-shared conversations contribute to its adaptability and the potential for continuous model improvement. With its customizable and versatile capabilities, Vicuna-13B plays a crucial role in the open-source LLM landscape.

6. BERT

The foundational technology behind LLMs is a neural architecture known as the transformer, which was pioneered in 2017 by Google researchers in their paper “Attention is All You Need”. Among the earliest experiments to showcase the potential of transformers was BERT.

Introduced by Google in 2018 as an open-source LLM, BERT (short for Bidirectional Encoder Representations from Transformers) quickly emerged as a leader, demonstrating state-of-the-art performance across various natural language processing tasks.

Due to its pioneering features during the nascent stages of LLM development and its open-source framework, BERT has become one of the most popular and extensively utilized LLMs. For instance, in 2020, Google revealed that it had integrated BERT into Google Search across more than 70 languages.

Presently, there exist thousands of open-source, freely available pre-trained BERT models tailored for specific applications, including sentiment analysis, clinical note comprehension, and identification of toxic comments.

7. GPT-NeoX and GPT-J

GPT-NeoX and GPT-J, developed by researchers at EleutherAI, a non-profit AI research lab, serve as excellent open-source alternatives to GPT.

While GPT-NeoX boasts 20 billion parameters and GPT-J features 6 billion parameters, both models exhibit high accuracy despite their smaller parameter sizes. Although many advanced LLMs can accommodate over 100 billion parameters, GPT-NeoX and GPT-J are capable of delivering reliable results.

These models have undergone training using 22 meticulously curated datasets sourced from a diverse array of origins, thereby enabling their application across various domains and use cases. Notably, unlike GPT-3, GPT-NeoX and GPT-J have not been trained with RLHF (reward learning from human feedback).

From text generation and sentiment analysis to research and marketing campaign development, GPT-NeoX and GPT-J are versatile enough to tackle any natural language processing task effectively.

8. OPT-175B

The launch of the Open Pre-trained Transformers Language Models (OPT) in 2022 marked a significant stride in Meta’s strategy to promote openness in the LLM landscape through open source.

OPT encompasses a collection of decoder-only pre-trained transformers, ranging from 125M to 175B parameters. Among them, OPT-175B stands out as one of the most advanced open-source LLMs available, boasting performance comparable to that of GPT-3. Both the pre-trained models and their source code are accessible to the public.

However, if you’re considering establishing an AI-driven enterprise centered around LLMs, it’s advisable to explore alternative options. This is because OPT-175B is released under a non-commercial license, permitting its utilization solely for research purposes.

Alternatives to open-source LLMs

While open-source LLMs have such advantages, it can’t be ingored that many people choose paid LLMs, so here are some reasons of why:

Certainly, here are some potential drawbacks or challenges associated with open-source LLMs:

1. Limited support: Open-source projects may lack comprehensive customer support compared to proprietary solutions, which could result in slower response times to issues or questions.
2. Complexity: Customizing and deploying open-source LLMs can be complex, requiring significant technical expertise and resources.
3. Security risks: While open-source projects undergo community scrutiny, they may still be vulnerable to security risks if not regularly updated or maintained.
4. Lack of proprietary features: Proprietary LLMs may offer unique features or optimizations not available in open-source alternatives, potentially limiting functionality or performance.
5. Dependency on community contributions: The development and improvement of open-source LLMs depend on community contributions, which can vary in quality and consistency.
6. Legal considerations: Some open-source licenses may impose restrictions on commercial usage or require attribution, which could impact business strategies or licensing agreements.
7. Integration challenges: Integrating open-source LLMs into existing systems or workflows may present compatibility issues or require additional development effort.
8. Limited documentation: Open-source projects may have sparse or outdated documentation, making it more challenging for users to understand and utilize the software effectively.
9. Scalability concerns: Scaling open-source LLMs to handle large volumes of data or increased workload demands may require additional infrastructure investments or optimizations.
10. Uncertain roadmap: The future development direction of open-source LLMs may be less predictable compared to proprietary solutions, potentially affecting long-term planning or investment decisions.

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What Is the Evaluation Criteria for Ranking the Top LLMs

Evaluating and ranking the top LLMs requires consideration of several criteria to ensure their suitability for specific use cases. The evaluation criteria include the open-source LLMs leaderboard, model size and computational efficiency, accuracy and language understanding, and customization and adaptability.

Open Source LLMs Leaderboard

LLM leaderboards are ranking systems that evaluate and compare different language models based on their performance on various NLP tasks. It provides a standardized framework for assessing the capabilities of language models and helps researchers and practitioners identify state-of-the-art models.

LLM leaderboards typically rank models based on their performance on multiple-choice benchmark tests and crowdsourced A/B preference testing. They evaluate models on tasks such as text generation, language understanding, translation, sentiment analysis, and question answering.

Model Size and Computational Efficiency

Model size and speed are important when looking at LLMs. The size depends on how many parts it has. Bigger models can do more but need more resources to work well.

Developers need to check their tools like GPUs and CPUs to pick the right model size. Small models can work okay without needing lots of resources. But big models are better, needing strong hardware.

Balancing size and speed helps LLMs perform well without costing too much. Developers should think about what they need and what tools they have to choose the best model size.

Accuracy and Language Understanding

Accuracy and understanding words are important for judging LLMs. These aspects affect how well a model produces fitting text.

LLMs need to be precise in processing and creating human-like language. To achieve this, they should be trained on varied data and involve human input for adjustments. Precise LLMs grasp user questions and give appropriate replies.

Understanding language is vital for LLMs to create relevant text. They must capture language details to offer exact and clear responses.

By checking accuracy and language understanding in LLMs, developers can confirm if the models produce top-notch text for different language tasks.

Customization and Adaptability

Customizing and adapting LLMs is crucial. Tailoring models to tasks boosts their performance. Open-source LLMs give access to source code and data for fine-tuning. Customization enhances models in certain areas.

Adaptability is vital for handling various cases. LLMs must learn from new data and adjust to input changes. This flexibility aids integration into existing systems. Evaluating customization helps choose models aligning with specific needs, ensuring flexibility for applications.

How to Improve Open Source LLMs

Improving open-source LLMs involves implementing specific techniques and approaches to enhance their performance and capabilities. Below are some strategies that can be employed to improve open-source LLMs:

• Fine-tuning techniques: Fine-tuning LLMs with task-specific data and human feedback can improve their performance in specific domains or tasks.
• Leveraging cloud services: Utilizing cloud services for scalability and deployment can enhance the accessibility and usability of open-source LLMs.
• Implementing security measures: Ensuring data protection and addressing ethical concerns are essential in improving the trustworthiness and reliability of open-source LLMs.

By implementing these strategies, developers can enhance the performance, scalability, and security of open-source LLMs, making them more effective for various applications.

Fine-Tuning Techniques for Better Performance

Fine-tuning improves LLM performance by training it on task-specific data or human feedback. Developers adapt LLM to enhance accuracy. This involves providing extra data related to the task or domain, obtained through collection or existing datasets. Human feedback refines LLM responses.

Developers customize LLMs for tasks, optimizing their accuracy and usability in real-world applications. Fine-tuning is crucial for improving open-source LLMs.

Leveraging Cloud Services for Scalability

Cloud services are a good option for using open-source LLMs. This helps developers scale their LLMs easily.

These services provide resources for training and running LLMs effectively. Developers can handle more work with scalability, ensuring good performance. Cloud platforms make deployment simple for integrating LLMs into systems.

Using cloud services enhances LLM scalability and availability. It helps manage big applications well with steady performance. This method makes it easy to deploy and use LLMs, reaching more users and uses.

Implementing Security Measures for Data Protection

Implementing safety steps is vital to protect data and address ethics when using open-source LLMs. Developers need to focus on safeguarding information by using encryption, access control, and data anonymization methods. These actions help secure user data and prevent unauthorized entry. It’s also essential for developers to follow ethical guidelines to ensure responsible use and minimize biases or harmful results. By incorporating strong safety measures, developers can establish trust in open-source LLMs and guarantee ethical deployment of these models. Data protection and ethical alignment are crucial aspects for users and organizations utilizing LLMs for different purposes.

Challenges and Solutions in Using Open Source LLMs

Open-source LLMs have benefits and challenges for developers. Challenges include bias, technical limits, and model enhancement. Solutions involve diverse data for bias, efficient computing for large models, and feedback loops for improvement.

Facing these issues helps developers use open-source LLMs effectively in their applications.

Handling Bias and Ethical Concerns

Addressing bias and ethical concerns is a crucial aspect of working with open-source LLMs. LLMs can inadvertently amplify biases present in the training data, leading to biased outputs and potential harm. Developers must actively address and mitigate these issues.

One solution is to ensure the training data is diverse and representative of different demographics and perspectives. Additionally, incorporating human feedback during the fine-tuning process can help identify and rectify biased outputs. Continuous alignment with ethical guidelines and standards is essential to maintain responsible usage of LLMs and mitigate potential harm.

By actively addressing bias and ethical concerns, developers can ensure the fairness and inclusivity of their open-source LLMs. This approach promotes responsible AI development and deployment, creating models that benefit a wider range of users and applications.

Overcoming Technical Limitations

To enhance open-source LLMs, solve tech issues like speed and resources for better performance. Use GPUs and CPUs efficiently to handle model computations well. Also, balance model size with available resources for optimal deployment. Enhance LLM accessibility and usability by overcoming technical challenges effectively.

Ensuring Continuous Model Improvement

Continuous model improvement is crucial for open-source LLMs to stay useful and meet user needs. Updating models regularly enhances their accuracy and understanding.

One way to improve continuously is by using feedback loops that collect user input and integrate it into the model. This helps models learn from users and enhance their results over time.

Model size and parameters are also important for continuous enhancement. Developers need to balance model size and performance, choosing the right size for effective training and use.

By focusing on ongoing improvement, developers can boost the effectiveness and value of open-source LLMs for various applications.

Conclusion

In conclusion, the landscape of Open Source LLMs for 2024 is rich with innovative offerings like Falcon 180B, LLaMA 2, BLOOM, GPT-NeoX, GPT-J, Vicuna 13-B, OPT-175B, and XGen-7B. These models exhibit exceptional capabilities in text generation, language understanding, and adaptability, setting new benchmarks in the NLP domain. As the industry moves towards larger models for commercial use, leveraging the right mix of parameters and human feedback will be crucial for continued advancements in generative AI.

Frequently Asked Questions

What makes an LLM “open source”?

An LLM is considered “open source” when its source code and training data are made publicly available, allowing developers to access, modify, and contribute to the model’s development.

What are the upcoming trends in open source LLMs for 2024?

In 2024, open-source LLMs are expected to continue evolving and pushing the boundaries of generative AI.

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