Large language models (LLMs) are changing the way we use artificial intelligence by making it easier to understand and generate human-like text. These models are built on neural networks and trained on massive amounts of data. This article looks at the top 10 open-source LLMs that are leading the way in 2024. These models are free to use and can be customized for different tasks, making them accessible to everyone from researchers to businesses.
Key Takeaways
- Open-source LLMs are making advanced AI technology accessible to everyone.
- These models can be customized for various tasks, from translation to text generation.
- Using open-source LLMs can save time and money compared to building models from scratch.
- Community support and collaboration help improve these models continuously.
- Open-source LLMs promote transparency and ethical AI practices.
1. LLaMA 2
LLaMA 2, short for Large Language Model Meta AI, is a powerful open-source language model developed by Meta AI. It builds upon its predecessor, LLaMA, with significant improvements in efficiency and scalability.
LLaMA 2 is available in various sizes, including 7 billion, 13 billion, and 70 billion parameters, making it adaptable to different computational needs. This flexibility allows it to be used in a wide range of applications, from lightweight tasks to more demanding ones.
Key Features
- Varied Model Sizes: Available in 7B, 13B, and 70B parameters.
- Advanced Training Techniques: Trained on a diverse and extensive dataset for better language understanding.
- Safety and Reliability: Designed to minimize biases and misinformation.
Deployability
- Scalable Solutions: Suitable for both small and large-scale applications.
- Platform Compatibility: Optimized for major cloud and AI platforms.
- Efficiency and Speed: Engineered for rapid processing even at larger sizes.
LLaMA 2 stands out for its versatility and power, making it a top choice for researchers and developers in the field of natural language processing.
2. BLOOM
In 2022, BLOOM was developed as an autoregressive Large Language Model (LLM) by a global team led by Hugging Face. This model can generate text by extending a given prompt using a vast amount of text data. BLOOM is a unique model born from a global collaboration led by Hugging Face. It boasts 176 billion parameters and can write fluently in 46 human languages and 13 programming languages.
BLOOM is open-source, meaning anyone can access its training data and source code to run, study, and improve it. This transparency has made it a popular choice among researchers and developers. Hugging Face users can use BLOOM for free.
Features
- Efficiency: BLOOM emphasizes efficiency and scalability, making it suitable for large-scale NLP applications.
- Advanced Algorithms: It uses cutting-edge algorithms to perform tasks like text summarization and language translation.
- Community Support: With backing from the open-source community, BLOOM continues to evolve, driving innovation in NLP.
BLOOM’s release marked a significant milestone in making generative AI more accessible. Its ability to generate coherent and accurate text in multiple languages has set a new standard for open-source LLMs.
3. BERT
BERT, which stands for Bidirectional Encoder Representations from Transformers, is a groundbreaking model in the field of natural language processing (NLP). Developed by Google in 2018, BERT introduced a new way of understanding context in text by looking at both the words before and after a target word. This bidirectional approach allows BERT to grasp the full meaning of words in a sentence, making it highly effective for various NLP tasks.
Features
- Contextual Understanding: BERT excels in understanding the context and semantics within textual data due to its bidirectional architecture.
- Pretrained Models: Google’s BERT comes with pretrained models for various languages and domains, facilitating transfer learning and adaptation to specific tasks.
- Wide Applicability: BERT is used in tasks such as sentiment analysis, question answering, and named entity recognition, showcasing its broad utility.
Uses and Applications
BERT is widely used for a variety of NLP jobs because of its adaptability. It is used in text categorization, question answering, named entity recognition (NER), and sentiment analysis. Companies incorporate BERT into recommendation engines, chatbots, and search engines to improve user experiences by producing natural language with more accuracy.
BERT was pre-trained on a large corpus of data then fine-tuned to perform specific tasks along with natural language inference and sentence text similarity. It has become a cornerstone in the NLP community due to its versatility and effectiveness.
Conclusion
BERT has revolutionized the way machines understand human language. Its bidirectional approach and extensive pretraining make it a powerful tool for a wide range of applications, from improving search engine results to enhancing the capabilities of chatbots.
4. Falcon 180B
Falcon 180B is an advanced open large language model developed by the Technology Innovation Institute. It boasts 180 billion parameters and was released in September 2023. This model is trained on a massive dataset of 3.5 trillion tokens, making it one of the most powerful open-source LLMs available.
Falcon 180B has already outperformed other notable models like LLaMA 2 and GPT-3.5 in various natural language processing (NLP) tasks. Hugging Face suggests that Falcon 180B can compete with Google’s PaLM 2, the LLM that powers Google Bard, due to its impressive processing capabilities.
Despite its power, Falcon 180B requires significant computational resources to operate efficiently. However, it is free for both commercial and research use, making it accessible to a wide range of users.
Features
- Massive Parameter Size: Falcon 180B’s large parameter size allows it to capture intricate linguistic patterns and nuances.
- Enhanced Learning: With extensive training data and robust architecture, Falcon 180B demonstrates superior learning capabilities, particularly in language-related tasks.
- Scalability: Despite its size, Falcon 180B maintains efficiency and scalability, making it suitable for deployment in diverse NLP applications.
Deployability
- Resource Intensive: Given its vast number of parameters, Falcon 180B requires significant computational resources, making it more suitable for organizations with access to high-powered computing infrastructure.
- Versatile Integration: Despite its size, Falcon 180B has been structured for ease of integration into existing systems, supported by a community that contributes to its ongoing development and optimization.
- Performance-Oriented: The model’s design and capabilities focus on delivering high-quality output, making it a valuable tool for research and commercial applications that demand the best in language comprehension and generation.
Falcon 180B represents the cutting edge of open-source LLMs, combining exceptional language processing capabilities with the scalability and support needed to tackle today’s most demanding AI tasks.
5. OPT-175B
In 2022, Meta made a big move by releasing the Open Pre-trained Transformers Language Models (OPT). This was part of their goal to make the LLM field more open and accessible. OPT-175B is the most powerful model in this series, with 175 billion parameters. It’s an open-source model that performs similarly to GPT-3. You can access both the source code and the pre-trained models.
However, if you’re thinking about using OPT-175B for a business, you might want to reconsider. The model is only available under a non-commercial license, which means it’s mainly for research purposes.
Features
- Precision and Efficiency: OPT-175B is designed to handle complex language tasks with high precision and efficiency.
- Fluency and Coherence: The model generates text that is both fluent and coherent, making it suitable for various applications.
- State-of-the-Art Performance: OPT-175B achieves top performance in language benchmarks, making it one of the leading open-source LLMs.
6. XGen-7B
Salesforce entered the LLM race with the release of XGen-7B in July 2023. Unlike many open-source LLMs that provide limited information with short prompts, XGen-7B supports longer context windows. The most advanced version, XGen-7B-8K-base, allows for an 8K context window, which includes the cumulative size of the input and output text.
XGen-7B is designed with efficiency in mind, using only 7 billion parameters for training. This is much less than other powerful open-source LLMs like LLaMA 2 or Falcon. Despite its smaller size, XGen-7B can still deliver excellent results. The model is available for commercial and research purposes, except for the XGen-7B-{4K,8K}-inst variant, which is trained on instructional data and RLHF and is released under a noncommercial license.
Features
- Versatility: XGen-7B demonstrates versatility and adaptability, leveraging advanced transformer architectures to excel in diverse NLP tasks.
- Text Classification: This model is proficient in tasks such as text classification, sentiment analysis, and document summarization, showcasing its broad applicability.
- Ease of Use: With user-friendly interfaces and extensive documentation, XGen-7B is accessible to both novice and experienced practitioners in the field of NLP.
Uses and Applications
Applications for XGen-7B include dialogue systems, story development, and the production of creative content. Companies create product descriptions, marketing material, and user-specific information using XGen-7B. Researchers also use XGen-7B for applications related to creative writing and language modeling.
XGen-7B is a powerful tool for both businesses and researchers, offering a blend of efficiency and versatility that makes it stand out in the crowded field of open-source LLMs.
7. GPT-NeoX
GPT-NeoX is an open-source language model developed by EleutherAI, a nonprofit AI research organization. Released in March 2021, it was the largest open-source GPT-3-style language model at the time. GPT-NeoX boasts 20 billion parameters, making it a powerful tool for various natural language processing (NLP) tasks.
Features
- Community-Driven Development: GPT-NeoX is a product of community-driven efforts, aiming to rival proprietary models in performance and scalability.
- Innovative Architecture: With its advanced architecture and extensive training data, GPT-NeoX excels in tasks like text generation and dialogue systems.
- Continuous Improvement: Supported by a vibrant community, GPT-NeoX is continuously updated with the latest advancements in NLP research.
Uses and Applications
GPT-NeoX can be used for a wide range of NLP tasks, including:
- Text generation
- Sentiment analysis
- Research
- Marketing campaign planning
GPT-NeoX and its sibling model GPT-J, which has 6 billion parameters, are available for free through the NLP Cloud API. This makes them accessible for both commercial and research purposes.
GPT-NeoX is trained on 22 high-quality datasets from diverse sources, ensuring its versatility and effectiveness across different domains.
8. Vicuna 13-B
Vicuna-13B is an open-source conversational model derived from the LLaMa 13B model. It has been fine-tuned using user-shared conversations collected from ShareGPT. Vicuna-13B is a groundbreaking open-source chatbot with numerous applications across various industries, including customer service, healthcare, education, finance, and travel/hospitality.
A preliminary evaluation using GPT-4 as a judge showed that Vicuna-13B achieved more than 90% of ChatGPT and Google Bard quality. It outperformed other models like LLaMa and Alpaca in more than 90% of cases.
Features
- Efficiency and Accuracy: Vicuna 13-B prioritizes efficiency and accuracy, making it well-suited for applications requiring nuanced understanding of textual data.
- Focused Development: Developed with a focus on specific NLP tasks, Vicuna 13-B delivers robust performance in areas such as language modeling and text completion.
- Customization Options: With customizable parameters and fine-tuning capabilities, Vicuna 13-B offers flexibility to adapt to diverse use cases and domains.
Vicuna-13B is an intelligent chatbot with a plethora of uses; a few are shown below in various industries, including customer service, healthcare, education, finance, and travel/hospitality.
9. YI 34B
YI 34B is a cutting-edge language model developed by China’s 01 AI. Currently, it holds the top spot on the Hugging Face Open LLM leaderboard. This model is designed to be bilingual, supporting both Chinese and English languages. Initially, it had a 4K token context window, but now it can be trained on up to 32K tokens.
The company has also released a 200,000 token version of the 34B model, which is available for both commercial and research purposes. With 3 trillion tokens in its training data, YI 34B excels in arithmetic and coding tasks. The company has provided benchmarks for both the supervised fine-tuned conversation models and the base models. Additionally, there are multiple 4-bit and 8-bit versions available.
Features
- Massive Parameter Size: YI 34B boasts a massive parameter size, enabling it to capture intricate linguistic nuances and generate contextually relevant text.
- Robust Architecture: With its robust architecture and extensive fine-tuning, YI 34B demonstrates superior performance in language-related tasks, including text generation and sentiment analysis.
- Scalability: Despite its size, YI 34B maintains scalability and efficiency, making it suitable for deployment in resource-constrained environments.
The YI 34B-200K’s performance improved by 10.5%, rising to new heights in the open-source LLM domain.
10. Mixtral 8x7B
Mixtral 8x7B, introduced by Mistral AI in December 2023, is a decoder-only sparse mixture-of-experts network. This model is licensed under Apache 2.0 and is designed to be both powerful and efficient.
Key Features
- Performance and Efficiency: Mixtral 8x7B offers a compelling blend of performance and efficiency, making it an attractive choice for various NLP applications.
- Innovative Training: Leveraging innovative training strategies and diverse datasets, Mixtral 8x7B achieves impressive capabilities in language understanding and generation.
- Accessibility: With accessible documentation and pretrained models, Mixtral 8x7B is accessible to a wide range of users, facilitating experimentation and research in the field of NLP.
Benchmark Performance
Mixtral 8x7B excels in several benchmarks, including ARC, HellaSwag, MMLU, and TruthfulQA. It offers six times faster inference than LLaMA 2 70B and outperforms GPT 3.5 in most areas except for the Mt Bench score. Additionally, it exhibits less bias on the BBQ benchmark and boasts multilingual capabilities in English, French, Italian, German, and Spanish.
Mixtral 8x7B is a versatile model that balances high performance with efficiency, making it suitable for a wide range of applications.
Technical Specifications
| Feature | Description |
|---|---|
| Parameters | 8×7 billion |
| Architecture | Decoder-only sparse mixture-of-experts |
| License | Apache 2.0 |
| Release Date | December 2023 |
Community and Support
Mistral AI continually enhances Mixtral’s linguistic capabilities to cater to a diverse range of applications and users. The model benefits from community-driven development, ensuring it remains at the forefront of LLM technology.
Conclusion
Open-source large language models (LLMs) are changing the game in the world of artificial intelligence. They offer free access, customization, and transparency, making advanced AI tools available to everyone. These models are not just for big companies anymore; they are for researchers, developers, and even small businesses. As we move forward, the open-source community will continue to drive innovation, making AI more accessible and useful for a wide range of applications. The future of AI looks bright, thanks to the collaborative efforts of people around the world who are committed to sharing knowledge and improving technology.
Frequently Asked Questions
What are open-source LLMs?
Open-source LLMs are large language models that are freely available for anyone to use, modify, and share. They are trained on huge amounts of text data to generate human-like language.
Why should I use open-source LLMs?
Open-source LLMs are free to use and can be customized for your specific needs. They also promote transparency, as you can see and modify the code.
What are some popular open-source LLMs?
Some popular open-source LLMs include LLaMA 2, BLOOM, BERT, Falcon 180B, OPT-175B, XGen-7B, GPT-NeoX, Vicuna 13-B, YI 34B, and Mixtral 8x7B.
How do open-source LLMs help in AI development?
They allow developers to build and improve AI applications without starting from scratch. This saves time and resources, and fosters innovation.
Are there any costs associated with using open-source LLMs?
While the models themselves are free, you might need powerful computers to run them, which can be costly.
Can I modify open-source LLMs for my own projects?
Yes, you can change and adapt open-source LLMs to fit your specific needs. This is one of the main benefits of open-source software.
What are the benefits of using open-source LLMs over proprietary ones?
Open-source LLMs are free, customizable, and transparent. You are not tied to any vendor, and you can modify the models as you wish.
How do I choose the right open-source LLM for my needs?
Consider your specific tasks, the model’s capabilities, and your available computing resources. Research and testing will help you find the best fit.
