Large language models, or LLMs, are essential to the present revolution in generative AI. Language models and interpreters are artificial intelligence (AI) systems that are based on transformers, a potent neural architecture. They are referred to as “large” because they contain hundreds of millions, if not billions, of pre-trained parameters derived from a vast corpus of text data.
In this article, we’ll look at the Top 10 open-source LLMs that will be available in 2024. Even though ChatGPT and (proprietary) LLMs have only been around for a year, the open-source community has made significant progress, and there are now numerous open-source LLMs available for various applications. Read on to discover the most popular!
LLM Models open-source
Top 10 Open-Source LLM Models
Top Open-Source Large Language Models For 2024
The basic models of widely used and well-known chatbots, such as Google Bard and ChatGPT, are LLM. In particular, Google Bard is built on Google’s PaLM 2 model, whereas ChatGPT is driven by GPT-4, an LLM created and owned by OpenAI. The proprietary underlying LLM of ChatGPT, Bard, and numerous other well-known chatbots are shared by them. This indicates that they belong to a business and that clients can only use them with a license that they have purchased. Along with rights, that license may also impose limitations on how the LLM is used and provide access to certain technical details.
However, open-source LLMs are a parallel trend in the Large Language Model that is quickly gaining traction. Open-source LLMs promise to improve accessibility, transparency, and innovation in the rapidly expanding field of generative AI and LMMs in response to growing concerns about the opaque nature and restricted availability of proprietary LLMs, which Big Tech companies like Microsoft, Google, and Meta primarily control.
1. LLaMA 2
Most top LLM firms developed their programs discreetly. Meta stands out. Meta provided crucial information about LLaMA 2 and its powerful, open-source alternative. LLaMA 2, a 7–70 billion-parameter generative text model, finished in July 2023. This model is for business and study. The RLHF improved it. Construct and train this text generation model to teach the chatbot natural language. Meta provides open, customizable LLaMA 2, Chat, and Code Llama.
Features:
- EleutherAI Development: LLaMA 2 is a product of the EleutherAI community, known for its commitment to open-source AI research.
- Improved Performance: Building upon its predecessor, LLaMA, this model incorporates advanced techniques to enhance language understanding and generation capabilities.
- Versatility: LLaMA 2 demonstrates proficiency across various NLP tasks, making it a versatile choice for researchers and developers alike.
2. BLOOM
In 2022, Flourish developed BLOOM, an autoregressive Large Language Model (LLM) that generates text by extending a prompt using large amounts of textual data. Over 70 countries’ experts and volunteers developed the project in one year. The open-source LLM BLOOM model includes 176 billion parameters. It writes fluently and cohesively in 46 languages and 13 programming languages. BLOOM execution, evaluation, and improvement with training data and source code are public. Hugging Face users use BLOOM free.
Features:
- Efficiency: Developed by Google Research, BLOOM emphasizes efficiency and scalability, making it suitable for large-scale NLP applications.
- Advanced Algorithms: BLOOM leverages cutting-edge algorithms to achieve remarkable performance in tasks such as text summarization and language translation.
- Community Support: With support from the open-source community, BLOOM continues to evolve, driving innovation in the field of NLP.
3. BERT (Bidirectional Encoder Representations from Transformers)
LLM technology relies on BERT (Bidirectional Encoder Representations from Transformers) neural architecture. Google researchers released “Attention is All You Need.” in 2017. BERT was an early transformer test. The 2018 Google Language Model BERT is available as open-source software. It swiftly mastered natural language processing tasks.
Bert’s advanced early LLM development capabilities and open-source nature make it a popular Language Model (LLM). With Bert in 2020, Google Search is available in over 70 languages. Many pre-trained Bert models are open-source. These models help detect harmful comments, clinical notes, and sentiments.
Features:
- Contextual Understanding: BERT excels in understanding context and semantics within textual data, owing 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 finds applications in tasks such as sentiment analysis, question answering, and named entity recognition, showcasing its broad utility.
4. Falcon 180B
The new Falcon 180B indicates that the difference between proprietary and open-source large language models is fast narrowing if the Falcon 40B, which ranked #1 on Hugging Face’s scoreboard for big language models, wasn’t already impressive to the open-source LLM community. Falcon 180B, which was made available by the Technology Innovation Institute of the United Arab Emirates in September 2023, is being trained using 3.5 trillion tokens and 180 billion parameters. Hugging Face indicates that Falcon 180B can compete with Google’s PaLM 2, the LLM that runs Google Bard, given its amazing processing capacity. Falcon 180B has already surpassed LLaMA 2 and GPT-3.5 in some NLP tasks.
It’s crucial to remember that Falcon 180B needs significant processing power to operate while being free for usage in both commercial and research settings.
Features:
- Massive Parameter Size: Falcon 180B boasts a large parameter size, enabling 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.
5. OPT-175B
In 2022, Meta achieved a significant milestone with the publication of the Open Pre-trained Transformers Language Models (TLM), which was part of their aim to use open source to free the LLM race. OPT consists of a set of pre-trained transformers, decoder-only, with parameters ranging from 125M to 175B. The most potent brother is OPT-175B, an open-source LLM that is among the most sophisticated on the market and performs similarly to GPT-3. The public can access both the source code and the pre-trained models. But, you’d best think of another option if you’re planning to build an AI-driven business with LLMs, as OPT-175B is only available under a non-commercial license that permits the model’s use for research use cases.
Features:
- Precision and Efficiency: OPT-175B is designed to handle complex language processing tasks with precision and efficiency, catering to the demands of modern NLP applications.
- Fluency and Coherence: This model showcases remarkable fluency and coherence in generating text across various domains, reflecting its advanced training techniques.
- State-of-the-Art Performance: OPT-175B achieves state-of-the-art performance in language-related benchmarks, solidifying its position as a leading open-source LLM.
6. XGen-7B
Businesses are entering the LLM race at an increasing rate. Salesforce was among the latest to enter the market, with the release of its XGen-7B LLM in July 2023. The authors claim that the majority of open-source LLMs concentrate on offering lengthy responses with scant details (i.e., brief prompts with little context). XGen-7B is an attempt to create a tool that can handle larger context windows. Specifically, the most sophisticated variation of XGen (XGen-7B-8K-base) supports an 8K context window—that is, the whole amount of text in both the input and output.
While XGen only utilizes 7B parameters for training—much fewer than most powerful open-source LLMs like LLaMA 2 or Falcon—efficiency is another top objective. Even though XGen is small in size, it can nonetheless produce excellent results. With the exception of the XGen-7 B-{4K,8K}-inst version, which was trained using instructional data and RLHF and is made available under a noncommercial license, the model is available for both commercial and research use.
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.
7. GPT-NeoX and GPT-NeoX
Generated by scientists at the nonprofit AI research center EleutherAI, GPT-NeoX and GPT-J are two excellent open-source substitutes for GPT. There are 20 billion parameters in GPT-NeoX and 6 billion in GPT-J. These two LLMs are able to produce findings with a high degree of accuracy, even though the majority of advanced LLMs can be trained using more than 100 billion parameters. They can be used in many different domains and application situations because they were trained on 22 high-quality datasets from a variety of sources. GPT-NeoX and GPT-J, in contrast to GPT-3, have not been trained using RLHF.
GPT-NeoX and GPT-J can be used for any natural language processing activity, including research, marketing campaign planning, sentiment analysis, and text generation. With the NLP Cloud API, you can get both LLMs for free.
Features:
- Community-Driven Development: GPT-NeoX/J is a community-driven effort to develop an open-source LLM capable of rivaling proprietary models in performance and scalability.
- Innovative Architecture: With its innovative architecture and extensive training data, GPT-NeoX/J delivers impressive results across diverse NLP tasks, including text generation and dialogue systems.
- Continuous Improvement: Supported by a vibrant community of researchers and developers, GPT-NeoX/J undergoes continuous improvement, incorporating the latest advancements in NLP research.
8. Vicuna 13-B
Using user-shared conversations collected from ShareGPT, the LLaMa 13B model was refined to create the open-source conversational model Vicuna-13B. 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. According to an initial assessment using GPT-4 as a judge, Vicuna-13B surpassed other models such as LLaMa and Alpaca in more than 90% of cases, attaining over 90% quality of ChatGPT and Google Bard.
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.
9. YI 34B
YI 34B China’s 01 AI developed a new language model called Yi 34B. Right now, this model holds the top spot on the Hugging Face Open LLM leaderboard. The company’s goal is to develop bilingual models that are capable of speaking Chinese and English. The model may now be trained on up to 32K tokens, compared to its original 4K token context window.
It’s impressive that the company recently released a 200,000 token version of the 34B model. These models can be licensed for commercial usage and are available for research purposes. With 3 trillion tokens under its belt, the 34B model excels in arithmetic and coding. Benchmarks for both the supervised fine-tuned conversation models and the base models have been made available by the company. There are multiple 4-bit and 8-bit versions available for the model.
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.
10. Mixtral 8x7B
Mixtral 8x7B, unveiled by Mistral AI in December 2023, is a decoder-only sparse mixture-of-experts network licensed under Apache 2.0. It outperforms LLaMA 2 and GPT 3.5 on various benchmarks despite having a smaller parameter size. With only 12.9 billion parameters per token out of a total of 46.7 billion, Mixtral achieves comparable processing rates to a 12.9B model.
It’s ranked among the top 10 LLMs by the Hugging Face Open LLM Leaderboard, excelling in benchmarks like ARC, HellaSwag, MMLU, and TruthfulQA. Mixtral offers 6 times faster inference than LLaMA 2 70B and outperforms GPT 3.5 in most areas except for the Mt Bench score. It exhibits less bias on the BBQ benchmark and boasts multilingual capabilities in English, French, Italian, German, and Spanish. Mistral AI continually enhances Mixtral’s linguistic capabilities to cater to a diverse range of applications and users.
Features:
- Blend of Performance and Efficiency: Mixtral 8x7B offers a compelling blend of performance and efficiency, making it an attractive choice for various NLP applications.
- Innovative Training Strategies: 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.
Comparison of Popular LLM Models
Here’s a Comparison of Popular LLM Models:
| Model/Model Family Name | Created By | Sizes | Versions | Pretraining Data | Fine-tuning and Alignment Details | License | What’s Interesting | Architectural Notes |
|---|---|---|---|---|---|---|---|---|
| LLaMA 2 | EleutherAI | Not Specified | 2 | Large-scale text corpora | Not specified | MIT License | Advanced language understanding and generation capabilities | Architecture enhancements from LLaMA |
| BLOOM | Google Research | Not Specified | Not Specified | Large-scale text corpora | Advanced fine-tuning techniques | Apache License 2.0 | Efficiency and scalability | Cutting-edge algorithms for text summarization |
| BERT | Various (e.g., BERT-base, BERT-large) | Multiple | Large-scale text corpora | Extensive fine-tuning options | Apache License 2.0 | Bidirectional context understanding | Bidirectional Encoder Representations from Transformers | |
| Falcon 180B | Not specified | 180 billion parameters | Not Specified | Large-scale text corpora | Robust architecture | Not specified | Superior learning capabilities | Massive parameter size |
| OPT-175B | Not specified | 175 billion parameters | Not Specified | Large-scale text corpora | State-of-the-art fine-tuning | Not specified | Precision and efficiency | Remarkable fluency and coherence |
| XGen-7B | Not specified | 7 billion parameters | Not Specified | Large-scale text corpora | Versatile fine-tuning strategies | Not specified | Versatility and adaptability | Proficiency in diverse NLP tasks |
| GPT-NeoX/J | OpenAI | Not Specified | Not Specified | Large-scale text corpora | Community-driven improvements | MIT License | Rivaling proprietary models | Continuous community-driven development |
| Vicuna 13-B | Not specified | 13 billion parameters | Not Specified | Large-scale text corpora | Focused fine-tuning strategies | Not specified | Efficiency and accuracy | Customizable parameters and fine-tuning capabilities |
| YI 34B | Not specified | 34 billion parameters | Not Specified | Large-scale text corpora | Extensive fine-tuning options | Not specified | Massive parameter size | Superior performance in language-related tasks |
| Mixtral 8x7B | Not specified | 8×7 billion parameters | Not Specified | Large-scale text corpora | Innovative training strategies | Not specified | Blend of performance and efficiency | Accessibility and ease of use |
How to Choose right Open-Source LLM ?
Choosing the right open-source Large Language Model (LLM) involves considering several factors to ensure that it aligns with your specific needs and requirements. Here’s a guide on how to choose the right open-source LLM:
1. Define Your Use Case:
- Identify Your Requirements: Determine the specific tasks and applications you intend to use the LLM for, such as text generation, sentiment analysis, language translation, or document summarization.
- Consider Domain Specificity: Some LLMs might perform better in certain domains (e.g., healthcare, finance, legal) due to their training data or fine-tuning techniques. Consider whether your application requires domain-specific expertise.
2. Evaluate Model Capabilities:
- Performance Metrics: Review the model’s performance metrics on benchmark datasets relevant to your use case. Look for metrics such as accuracy, fluency, coherence, and efficiency.
- Task-Specific Performance: Assess the model’s performance on specific tasks through empirical evaluations or comparisons with existing benchmarks.
3. Examine Model Architecture and Features:
- Architectural Considerations: Understand the underlying architecture of the LLM, such as transformer-based models like GPT, BERT, or novel architectures like LLaMA. Consider whether the model architecture aligns with your requirements.
- Feature Set: Evaluate the model’s features, such as support for multi-task learning, fine-tuning capabilities, and adaptability to different input formats and languages.
4. Consider Scalability and Efficiency:
- Parameter Size: Larger models typically have more parameters, which may improve performance but also increase computational requirements. Assess whether the model’s parameter size is suitable for your hardware infrastructure and computational resources.
- Efficiency: Consider the model’s inference speed and resource utilization to ensure that it meets real-time or latency-sensitive application requirements.
5. Community Support and Documentation:
- Active Development: Choose a model that is actively maintained and supported by a vibrant community of developers and researchers. Active development ensures ongoing improvements, bug fixes, and updates.
- Comprehensive Documentation: Look for models with well-documented APIs, tutorials, and example code to facilitate model deployment, fine-tuning, and integration into your existing workflows.
6. Licensing and Legal Considerations:
- Open-Source License: Ensure that the model is released under an open-source license compatible with your organization’s policies and legal requirements. Common licenses include Apache License, MIT License, and GNU General Public License (GPL).
- Data Privacy and Compliance: Consider any data privacy and compliance requirements relevant to your use case, especially if the model will handle sensitive or regulated data.
7. Experiment and Benchmark:
- Conduct Experimentation: Experiment with multiple LLMs to compare their performance and suitability for your use case. Evaluate models on representative datasets and tasks to make informed decisions.
- Benchmarking: Benchmark the selected models against each other and against baseline models or industry standards to validate their performance and identify the best-performing option.
8. Plan for Future Growth:
- Scalability: Choose a model that can scale with your organization’s growth and evolving needs. Consider factors such as support for distributed training, model parallelism, and efficient model serving infrastructure.
- Flexibility: Select a model that offers flexibility for future adaptations and extensions, such as fine-tuning on domain-specific data or integrating with custom pipelines and applications.
By considering these factors and conducting thorough evaluations, you can choose the right open-source LLM that best fits your requirements and empowers you to leverage advanced language processing capabilities effectively.
Conclusion
The movement of open-source LLMs is quite interesting. Given their quick development, it appears that large companies with the resources to create and employ these potent instruments won’t always control the generative AI market.
There are far more open-source LLMs than the ten that we have examined so far, and they are expanding quickly. We will keep you updated on the most recent developments in the LLM industry by offering tutorials, articles, and courses.