SLM and LLM... My Top 10 in July 2024
?? Fabrizio Degni
Chief of Artificial Intelligence | E.N.I.A. Innovation & Digital Transformation | LA ISO/IEC 42001 | CCEM | MCE | CTU | IEEE WGM | IE-MBA Candidate | DCS Candidate | Gartner IT Community Ambassador
Large Language Models (LLMs) and Small Language Models (SLMs), offer varying capabilities and serve different use cases based on their design and computational requirements. The comparison highlights the Top 10 models in each category, providing detailed insights into their use cases, limitations, and licensing.
LLMs, characterized by their massive parameters and extensive training data, excel in tasks requiring deep understanding and high computational power. They are often utilized in applications such as conversational AI, content generation, and complex data analysis. However, their substantial resource demands can make them unsuitable for low-latency or resource-constrained environments.
On the other hand, SLMs, the ones I prefer, are designed for efficiency and lightweight deployment, making them ideal for applications that require quick responses and can operate within limited computational resources. These models are particularly useful for edge AI applications, mobile AI solutions, and real-time processing tasks.
What should you gain from reading this article?
Insight into Top Models: Information about my Top 10 models in each category, including their organizational affiliations, release dates, parameter sizes, licensing types, and primary applications.
Basic Guidance for Application: Practical insights that can help you decide which type of model to use for specific applications, whether you need a model for high-complexity tasks or efficient, real-time processing.
Resource Links: Direct access to further details and resources for each model, allowing for deeper exploration and understanding of their functionalities and how to implement them.
Discussion Platform: A place to discuss these models with other enthusiasts and experts, the section of the comments for example!
Index of Foundation Models
Large Language Models (LLMs)
Small Language Models (SLMs)
A Disclaimer...
For some models both categories are valid, don't be worried to see a model you thought was an SLM on the LLM side and vice-versa: they are distributed in different versions and various parameters. I just picked up and classified them according to my preference/opinion. The list also isn't following a specific ranking: it is just a list without priority in the order.
I am also aware of the exclusions: please refer to the great HuggingFace catalogue for a more (https://huggingface.co/models).
Large Language Models (LLMs)
1. GPT-4o
- Organization: OpenAI
- Release Date: May 2024
- In a nutshell: GPT-4o is the latest iteration of OpenAI's Generative Pre-trained Transformer series, designed for a wide range of natural language processing tasks.
- Introduction: GPT-4o represents OpenAI’s continued innovation in the field of natural language processing (NLP). Building on the successes of its predecessors, GPT-4o brings enhanced capabilities in understanding and generating human-like text. This model is designed to handle a broad spectrum of tasks, from generating coherent essays and articles to engaging in nuanced conversations and providing detailed programming assistance. One of its standout features is its ability to perform zero-shot and few-shot learning, allowing it to handle tasks it hasn't been explicitly trained on with minimal examples. However, the model's high computational requirements mean it is best suited for applications where latency and resource consumption are not critical constraints. OpenAI continues to focus on ensuring the ethical use of its technologies, incorporating safety measures to minimize the risk of misuse. GPT-4o is poised to push the boundaries of what AI can achieve in language-related tasks, making significant strides in areas such as automated content creation, advanced chatbots, and even aiding in complex scientific research by generating hypotheses or summarizing large volumes of text.
- Website: https://www.openai.com/
- GitHub: Not available
- License: Proprietary
- Use Cases: Conversational AI, content generation, language translation, summarization, and programming assistance.
- Not Suitable For: Low-latency applications due to computational complexity and resource demands.
2. Claude 3
- Organization: Anthropic
- Release Date: March 2024
- In a nutshell: Claude 3 is Anthropic's ethical-focused AI model, emphasizing safe and reliable AI behavior.
- Introduction: Claude 3 by Anthropic represents a significant advancement in ethical AI development. Named presumably after Claude Shannon, the father of information theory, Claude 3 is designed with a strong emphasis on creating AI systems that are safe, interpretable, and aligned with human values. Anthropic has built Claude 3 with robust safety features to ensure that it can be deployed in sensitive areas such as content moderation and dialogue systems without posing unintended risks. The model excels in applications that require a high degree of reliability and ethical considerations, making it ideal for use in sectors where the consequences of AI decisions are particularly impactful. However, its design prioritizes ethical behavior over raw processing speed, making it less suitable for real-time applications that require instantaneous responses. Claude 3 is particularly adept at maintaining contextual understanding in conversations, ensuring that responses are not only accurate but also appropriate and considerate. This makes it a powerful tool for developing AI systems that need to navigate complex social interactions and ethical dilemmas, contributing to safer and more reliable AI deployments across various industries.
- Website: [Anthropic Claude 3](https://www.anthropic.com/)
- GitHub: Not available
- License: Proprietary
- Use Cases: Ethical AI development, dialogue systems, content moderation, and AI safety research.
- Not Suitable For: High-speed, real-time processing tasks due to focus on ethical and safe AI.
3. Grok-1
- Organization: xAI
- Release Date: November 2023
- Parameters: 314B
- In a nutshell: Grok-1 is xAI's advanced language model, designed for deep understanding and complex problem-solving.
- Introduction: Grok-1 is the flagship model from xAI, an organization founded with the goal of pushing the boundaries of artificial intelligence. With 314 billion parameters, Grok-1 is designed to excel in deep understanding and complex problem-solving tasks. The model’s architecture allows it to process vast amounts of information and generate highly sophisticated responses, making it ideal for applications in advanced data analysis, scientific research, and other fields that require deep comprehension and nuanced reasoning. One of the key features of Grok-1 is its ability to understand context and provide detailed, accurate answers across a wide range of topics. This makes it particularly useful for tasks such as technical documentation, legal analysis, and academic research. However, the model's large size and computational requirements mean it is not well-suited for mobile or edge applications where resources are limited. Grok-1 represents a significant leap forward in the capabilities of AI models, offering unparalleled performance for users who need to tackle complex and challenging problems.
- Website: [xAI Grok-1](https://www.xai.com/)
- GitHub: Not available
- License: Proprietary
- Use Cases: Advanced problem-solving, complex data analysis, and natural language understanding.
- Not Suitable For: Mobile and edge applications due to large model size.
4. LLAMA 3
- Organization: META
- Release Date: April 2024
- Parameters: 8B and 70B
- In a nutshell: Llama 3 by Meta AI is designed for research projects, educational tools, and small-scale enterprise solutions.
- Introduction: PaLM 2, developed by Google, is a cutting-edge large language model designed to handle a wide array of multimodal applications. With 340 billion parameters, PaLM 2 is engineered to excel in tasks that integrate text, images, and other data forms, providing a versatile tool for developers and researchers. The model's extensive training allows it to generate creative content, assist in language learning, and support automated research efforts by synthesizing information from multiple sources. PaLM 2 is particularly noted for its ability to generate coherent and contextually relevant text, making it ideal for applications such as automated content creation, sophisticated chatbot development, and educational tools. However, its substantial computational requirements mean it is best deployed in environments where resources are plentiful. This makes it less suitable for mobile devices or edge computing scenarios where power and memory are limited. Google's PaLM 2 stands out for its ability to seamlessly integrate different types of data, providing a powerful tool for applications that require a high level of contextual understanding and multimodal processing capabilities.
- Website: [META Website] (https://ai.facebook.com/)
- GitHub: [Meta AI GitHub] (https://github.com/facebookresearch/llama)
- License: Proprietary
- Use Cases: Research projects, educational tools, and small-scale enterprise solutions..
- Not Suitable For: Commercial product deployment due to non-commercial license.
5. Falcon 180B
- Organization: Technology Innovation Institute
- Release Date: September 2023
- Parameters: 180B
- In a nutshell: Falcon 180B is TII's robust model designed for high-scale research and technical applications.
- Introduction: Falcon 180B is a powerful language model developed by the Technology Innovation Institute (TII), featuring 180 billion parameters. This model is designed to support high-scale research and technical applications, making it an excellent choice for scientists and engineers who need to process and analyze large volumes of data. Falcon 180B is particularly well-suited for tasks such as writing technical documentation, conducting scientific research, and performing complex data analysis. The model’s ability to understand and generate detailed technical content makes it a valuable tool for experts in various fields who need to communicate complex ideas clearly and accurately. Additionally, its robust architecture ensures that it can handle large datasets and produce reliable results. However, the high computational demands of Falcon 180B mean that it is not suitable for consumer-grade devices or applications that require minimal resources. Instead, it is best utilized in environments where computational power and memory are abundant, such as research institutions and large enterprises. The open-source Apache 2.0 license ensures that Falcon 180B can be freely used, modified, and distributed, promoting collaboration and innovation within the scientific and technical communities.
- Website: [TII Falcon 180B](https://www.tii.ae/)
- GitHub: [TII Falcon 180B GitHub](https://github.com/tiiuae/falcon)
- License: Apache 2.0
- License Details: [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)
- Use Cases: Scientific research, technical documentation, and large-scale data analysis.
- Not Suitable For: Consumer-grade devices and applications requiring minimal computational resources.
6. Gemini 1.5
- Organization: Google DeepMind
- Release Date: February 2024
- In a nutshell: Gemini 1.5 is a sophisticated AI model by Google DeepMind, aimed at precision tasks in healthcare and finance.
- Introduction: Gemini 1.5 by Google DeepMind is a high-performance AI model tailored for applications that demand high accuracy and reliability. Leveraging advanced machine learning techniques, Gemini 1.5 excels in complex domains such as healthcare diagnostics and financial modeling. In healthcare, it aids in diagnosing medical conditions by analyzing patient data, images, and other diagnostic tools, offering support to medical professionals by providing highly accurate second opinions and predictive insights. In finance, Gemini 1.5 is used for modeling economic scenarios, forecasting market trends, and performing risk assessments, thus helping financial analysts and institutions make informed decisions. Its AI-driven simulations are also pivotal in fields like drug discovery and climate modeling, where precise predictions and analyses are critical. However, due to its high precision focus, Gemini 1.5 is not ideal for non-critical applications where such exacting standards are unnecessary. This model represents DeepMind’s commitment to advancing AI in ways that can significantly impact critical sectors, providing tools that support professionals in making better, data-driven decisions.
- Website: [Google DeepMind Gemini 1.5](https://deepmind.com/)
- GitHub: Not available
- License: Proprietary
- Use Cases: Healthcare diagnostics, financial modeling, and AI-driven simulations.
- Not Suitable For: Non-critical applications where high precision and reliability are not essential.
7. Inflection-2.5
- Organization: Inflection AI
- Release Date: March 2024
- In a nutshell: Inflection-2.5 focuses on personalized AI experiences, optimizing user interactions.
- Introduction: Inflection-2.5, developed by Inflection AI, is designed to enhance user interactions through highly personalized AI experiences. This model excels in applications that require deep understanding of user preferences and behaviors, such as AI assistants and customer service automation. Inflection-2.5 can learn and adapt to individual user needs, providing tailored responses and proactive assistance in various contexts, from scheduling and reminders to providing personalized content recommendations. In customer service, it can automate interactions, resolving issues efficiently while maintaining a high degree of personalization, thus improving customer satisfaction and reducing operational costs. Additionally, its predictive analytics capabilities enable businesses to forecast trends and behaviors, allowing for more informed strategic decisions. Despite its strengths, Inflection-2.5 is not designed for applications that demand extremely low latency, as its complex processing can introduce delays. Overall, Inflection-2.5 represents a significant step forward in making AI interactions more natural and effective, aligning with Inflection AI’s goal of creating AI that seamlessly integrates into everyday life to enhance user experiences.
- Website: [Inflection AI](https://www.inflection.ai/)
- GitHub: Not available
- License: Proprietary
- Use Cases: Personalized AI assistants, customer service automation, and predictive analytics.
- Not Suitable For: Applications requiring extremely low latency responses.
8. Phi-3
- Organization: Microsoft
- Release Date: April 2024
- Parameters: 3.8B
- In a nutshell: Phi-3 is a versatile model by Microsoft, designed to enhance enterprise-level AI applications.
- Introduction: Phi-3 by Microsoft is a robust AI model tailored for enterprise applications, aimed at enhancing productivity and decision-making processes within large organizations. With 3.8 billion parameters, Phi-3 provides a balanced blend of performance and efficiency, making it suitable for various enterprise-level tasks. This model is particularly effective in business intelligence, where it can analyze vast amounts of data to generate actionable insights, helping businesses make data-driven decisions. Phi-3 also supports software development by assisting in code generation, debugging, and documentation, thus speeding up the development lifecycle and improving software quality. In addition, its capabilities extend to customer relationship management (CRM), where it helps in understanding customer behaviors and optimizing marketing strategies. However, due to its relatively smaller size compared to other high-end models, Phi-3 may not be ideal for tasks that require extensive contextual understanding and deep learning capabilities. Nonetheless, Phi-3 stands out as a versatile tool for enterprises looking to leverage AI to enhance their operational efficiencies and strategic planning.
- Website: [Microsoft Phi-3](https://www.microsoft.com/)
- GitHub: Not available
- License: Proprietary
- Use Cases: Enterprise AI solutions, software development support, and business intelligence.
- Not Suitable For: High-complexity tasks requiring vast contextual understanding due to smaller parameter size.
9. Sora
- Organization: OpenAI
- Release Date: Announced February 2024
- Quick Intro: Sora is OpenAI's latest text-to-video, designed for immersive experiences.
- Introduction: Sora, currently under development by OpenAI, is a cutting-edge text-to-video generative AI model. It is designed to create videos from textual descriptions, extend existing video content, and potentially fill in missing frames within videos. While the full range of Sora's applications is still being explored, its potential uses span creative content generation, educational tools, and potentially other areas where video creation from text could be beneficial. Sora is not intended for applications requiring high precision or those involving critical decision-making processes
- Website: [OpenAI Sora](https://www.openai.com/)
- GitHub: Not available
- License: Proprietary
- Use Cases: Interactive learning platforms, virtual tutoring, and immersive gaming experiences.
- Not Suitable For: Industrial and high-stakes decision-making processes due to focus on interactive and educational contexts.
10. OPT-175B
- Organization: Meta
- Release Date: 2022
- Parameters: 175B
- In a nutshell: OPT-175B is Meta's large-scale open model for research and educational purposes.
- Introduction: OPT-175B, developed by Meta, is one of the largest language models available, featuring 175 billion parameters. This model is specifically designed to advance academic research and foster collaboration in the AI community. OPT-175B is ideal for use in research projects that require extensive data analysis and generation of detailed reports, aiding researchers in exploring new hypotheses and drawing insights from large datasets. It also serves as a valuable educational tool, helping students and educators by providing comprehensive explanations, generating learning materials, and assisting in the understanding of complex topics. Despite its impressive capabilities, the non-commercial license restricts its use to non-profit activities, making it unsuitable for commercial applications. This restriction ensures that OPT-175B remains a resource for advancing knowledge and fostering innovation within the academic and educational sectors. Meta's OPT-175B exemplifies the potential of large language models to contribute significantly to scientific and educational advancements, providing a powerful tool for those engaged in research and learning.
- Website: [Meta OPT-175B](https://ai.facebook.com/)
- GitHub: [Meta OPT-175B GitHub](https://github.com/facebookresearch/metaseq)
- License: Non-commercial license
领英推荐
- License Details: [Non-commercial license details](https://github.com/facebookresearch/metaseq/blob/main/LICENSE)
- Use Cases: Academic research, collaborative projects, and educational tools.
- Not Suitable For: Commercial applications due to non-commercial license restrictions.
1. Mistral 7B
- Organization: Mistral AI
- Release Date: September 2023
- Parameters: 7.3B
- In a nutshell: Mistral 7B is designed for efficient and lightweight applications, balancing power and resource usage.
- Introduction: Mistral 7B by Mistral AI is a small yet powerful language model, boasting 7.3 billion parameters. This model is engineered to provide robust performance while maintaining efficiency, making it an excellent choice for applications where computational resources are limited. Mistral 7B is particularly well-suited for edge AI applications, where its ability to operate on minimal hardware makes it ideal for deployment in mobile devices, IoT systems, and other resource-constrained environments. The model’s lightweight design does not compromise its ability to perform complex language processing tasks, allowing it to support a range of functionalities from voice recognition and text analysis to real-time translation and natural language understanding. This balance of power and efficiency ensures that Mistral 7B can provide valuable AI capabilities without the need for extensive computational infrastructure. However, its smaller parameter size means it might not handle large-scale data processing and high-complexity problem-solving tasks as effectively as larger models. Despite these limitations, Mistral 7B is a versatile and efficient model, making advanced AI accessible for applications where lightweight and mobile solutions are critical.
- Website: [Mistral AI](https://mistral.ai/)
- GitHub: Not available
- License: Proprietary
- Use Cases: Edge AI applications, mobile AI solutions, and lightweight language processing.
- Not Suitable For: Large-scale data processing and high-complexity problem-solving.
3. Llama 3
- Organization: Meta AI
- Release Date: April 2024
- Parameters: 8B and 70B
- In a nutshell: Llama 3 is Meta AI's advanced research model, focused on educational and experimental use.
- Introduction: Llama 3, developed by Meta AI, is a sophisticated language model tailored for research and educational purposes. Available in 8B and 70B parameter versions, Llama 3 is designed to support advanced academic and experimental projects. It provides researchers with powerful tools for conducting AI-driven studies, analyzing complex datasets, and generating comprehensive reports. Llama 3's capabilities extend to educational settings, where it can assist in creating interactive learning materials, providing detailed explanations, and supporting instructional activities. The model is also suitable for small-scale enterprise solutions, where its robust language processing can enhance productivity and innovation. However, the non-commercial license restricts its use to academic and research-oriented applications, making it unsuitable for commercial product deployment. This restriction ensures that Llama 3 remains a valuable resource for advancing knowledge and fostering innovation within the scientific community. Meta AI's commitment to open research is exemplified in Llama 3, providing a powerful and flexible tool for exploring new frontiers in artificial intelligence.
- Website: [Meta AI Llama 3](https://ai.facebook.com/)
- GitHub: [Meta AI GitHub](https://github.com/facebookresearch/llama)
- License: Non-commercial license
- License Details: [Non-commercial license details](https://github.com/facebookresearch/llama/blob/main/LICENSE)
- Use Cases: Research projects, educational tools, and small-scale enterprise solutions.
- Not Suitable For: Commercial product deployment due to non-commercial license.
4. Mixtral 8x22B
- Organization: Mistral AI
- Release Date: April 2024
- Parameters: 141B
- In a nutshell: Mixtral 8x22B is designed for multimodal AI integration, advanced robotics, and interactive AI systems.
- Introduction: Mixtral 8x22B by Mistral AI is a cutting-edge model designed for advanced multimodal AI applications. With a substantial 141 billion parameters, Mixtral 8x22B excels in integrating various types of data, such as text, images, and audio, to create cohesive and sophisticated AI systems. This model is particularly well-suited for advanced robotics, where it can enable machines to understand and interact with their environment in a more human-like manner. Its capabilities in multimodal AI integration make it ideal for developing interactive systems that require seamless interaction between different data forms, such as virtual assistants, smart home devices, and augmented reality applications. However, the high computational demands of Mixtral 8x22B mean that it is not suitable for resource-constrained environments, such as mobile devices or low-power edge computing scenarios. Instead, it is best utilized in settings where ample computational resources are available, such as research laboratories and large enterprises. Mistral AI's Mixtral 8x22B represents a significant advancement in the field of AI, providing powerful tools for creating intelligent systems that can interact with the world in more dynamic and sophisticated ways.
- Website: [Mistral AI](https://mistral.ai/)
- GitHub: Not available
- License: Proprietary
- Use Cases: Multimodal AI integration, advanced robotics, and interactive AI systems.
- Not Suitable For: Resource-constrained environments due to high computational demands.
5. Jamba
- Organization: AI21 Labs
- Release Date: March 2024
- Parameters: 52B
- In a nutshell: Jamba is designed for content creation, language translation, and conversational AI.
- Introduction: Jamba by AI21 Labs is a powerful language model with 52 billion parameters, designed to excel in content creation, language translation, and conversational AI applications. Jamba's advanced language processing capabilities make it an ideal tool for generating high-quality written content, such as articles, reports, and creative writing, assisting writers and content creators in producing engaging and well-structured text. In the realm of language translation, Jamba offers precise and contextually accurate translations, facilitating communication across different languages and cultures. Its conversational AI capabilities enable the development of sophisticated chatbots and virtual assistants that can understand and respond to user queries with a high degree of relevance and coherence. However, due to its computational complexity, Jamba is not well-suited for real-time, low-latency applications where instantaneous responses are required. AI21 Labs' Jamba is a versatile and powerful model that can significantly enhance productivity and creativity in various domains, providing robust AI solutions for businesses and individuals alike.
- Website: [AI21 Labs Jamba](https://www.ai21.com/)
- GitHub: Not available
- License: Proprietary
- Use Cases: Content creation, language translation, and conversational AI.
- Not Suitable For: Real-time, low-latency applications due to computational complexity.
6. Command R
- Organization: Cohere
- Release Date: March 2024
- Parameters: 35B
- In a nutshell: Command R is optimized for document summarization, semantic search, and personalized recommendations.
- Introduction: Command R, developed by Cohere, is a specialized language model with 35 billion parameters, designed to optimize document summarization, semantic search, and personalized recommendation systems. Command R's advanced capabilities in understanding and processing natural language make it an ideal tool for condensing large volumes of text into concise and informative summaries, enhancing information retrieval and knowledge management. Its semantic search functionality allows users to find relevant information quickly and accurately, even in large and complex datasets, by understanding the meaning and context of queries. Additionally, Command R's ability to provide personalized recommendations based on user behavior and preferences makes it valuable in e-commerce, content delivery, and other applications where tailored experiences are essential. However, the model may not be suitable for applications that require extensive customization and domain-specific knowledge, as its general-purpose design might not capture the nuances of highly specialized fields. Cohere's Command R represents a significant advancement in language processing, offering powerful tools to improve efficiency and user satisfaction across various applications.
- Website: [Cohere Command R](https://cohere.ai/)
- GitHub: Not available
- License: Proprietary
- Use Cases: Document summarization, semantic search, and personalized recommendations.
- Not Suitable For: Applications requiring extensive customization and domain-specific knowledge.
7. Gemma
- Organization: Google DeepMind
- Release Date: February 2024
- Parameters: 2B and 7B
- In a nutshell: Gemma is a compact model by Google DeepMind, designed for efficient processing in healthcare diagnostics, financial analytics, and automated customer support.
- Introduction: Gemma, developed by Google DeepMind, is a versatile and efficient AI model available in 2 billion and 7 billion parameter versions. Designed to handle specific, high-value tasks with remarkable efficiency, Gemma is particularly well-suited for applications in healthcare diagnostics, financial analytics, and automated customer support. In healthcare, Gemma aids in diagnosing diseases by analyzing patient data, images, and medical records, providing doctors with accurate and timely insights that can enhance patient care. Its financial analytics capabilities allow it to process vast amounts of financial data, predict market trends, and provide investment recommendations, thus supporting financial institutions in making informed decisions. Additionally, Gemma excels in automated customer support, where it can handle queries, resolve issues, and provide information with high accuracy, improving customer satisfaction and reducing operational costs. Despite its strengths, Gemma's smaller parameter size compared to other large models means it might not be suitable for tasks requiring extensive data analysis and deep contextual understanding. However, its compact and efficient design makes it an excellent choice for specific, targeted applications where speed and resource efficiency are crucial.
- Website: [Google DeepMind Gemma](https://www.kaggle.com/models/google/gemma)
- GitHub: Not available
- License: Proprietary
- Use Cases: Healthcare diagnostics, financial analytics, and automated customer support.
- Not Suitable For: High-complexity, large-scale data analysis due to relatively smaller parameter sizes.
8. XGen-7B
- Organization: Salesforce
- Release Date: July 2023
- Parameters: 7B
- In a nutshell: XGen-7B is designed by Salesforce for enhancing CRM, sales forecasting, and customer insights.
- Introduction: XGen-7B by Salesforce is a powerful AI model tailored to optimize customer relationship management (CRM) systems, sales forecasting, and customer insights. With 7 billion parameters, XGen-7B brings advanced language processing capabilities to the realm of business intelligence, helping companies better understand their customers and market dynamics. It enhances CRM platforms by automating data entry, analyzing customer interactions, and generating actionable insights, enabling businesses to provide personalized services and improve customer satisfaction. In sales forecasting, XGen-7B analyzes historical sales data, market trends, and external factors to predict future sales performance, helping businesses make strategic decisions and optimize inventory management. Additionally, its ability to derive deep customer insights from vast datasets allows companies to tailor their marketing strategies and product offerings to meet customer needs more effectively. However, XGen-7B's design is focused on text-based analysis and language processing, making it less suitable for tasks involving high-resolution image processing or complex multimodal AI applications. Salesforce's XGen-7B is a valuable tool for businesses looking to leverage AI to enhance their CRM and sales operations, driving growth and customer satisfaction.
- Website: [Salesforce XGen-7B](https://www.salesforce.com/)
- GitHub: Not available
- License: Proprietary
- Use Cases: CRM enhancements, sales forecasting, and customer insights.
- Not Suitable For: High-resolution image processing and advanced multimodal AI applications.
9. DBRX
- Organization: Databricks' Mosaic ML
- Release Date: March 2024
- Parameters: 132B
- In a nutshell: DBRX is a robust model by Databricks' Mosaic ML, designed for data engineering, machine learning pipelines, and big data analytics.
- Introduction: DBRX, developed by Databricks' Mosaic ML, is a highly capable AI model featuring 132 billion parameters. This model is specifically designed to support advanced data engineering, machine learning pipelines, and big data analytics, making it an indispensable tool for enterprises that need to process and analyze large volumes of data efficiently. DBRX excels in creating and managing complex data workflows, automating data cleaning, transformation, and integration tasks, which are essential for maintaining high-quality datasets for machine learning models. Its capabilities in building and optimizing machine learning pipelines enable data scientists to streamline model development, training, and deployment, significantly reducing the time and effort required to bring AI solutions to production. Additionally, DBRX's robust analytics capabilities allow businesses to extract valuable insights from big data, driving strategic decisions and uncovering new opportunities. However, due to its large model size and computational demands, DBRX is not suitable for lightweight, mobile, or edge AI applications. Instead, it is best utilized in environments with ample computational resources, such as large data centers and cloud-based platforms. Databricks' DBRX is a powerful model that empowers organizations to harness the full potential of their data, enhancing their ability to innovate and compete in the data-driven economy.
- Website: [Databricks Mosaic ML](https://databricks.com/)
- GitHub: Not available
- License: Proprietary
- Use Cases: Data engineering, machine learning pipelines, and big data analytics.
- Not Suitable For: Lightweight, mobile, or edge AI applications due to large model size and computational requirements.
10. Alpaca 7B
- Organization: Stanford CRFM
- Release Date: March 2023
- Parameters: 7B
- In a nutshell: Alpaca 7B by Stanford CRFM is designed for academic research, educational tools, and experimental AI applications.
- Introduction: Alpaca 7B, developed by Stanford's Center for Research on Foundation Models (CRFM), is a versatile and accessible AI model designed to support academic research, educational tools, and experimental applications. With 7 billion parameters, Alpaca 7B is optimized for tasks that require robust language understanding and generation, making it a valuable asset for researchers and educators. In academic research, Alpaca 7B can assist in analyzing large datasets, generating reports, and providing insights into complex subjects, facilitating groundbreaking research across various disciplines. Its application in educational tools helps create interactive learning environments, personalized tutoring systems, and automated content generation, enhancing the learning experience for students. Additionally, Alpaca 7B's capabilities in experimental AI applications allow developers to explore innovative uses of AI, test new hypotheses, and develop proof-of-concept projects. However, due to its non-commercial license, Alpaca 7B is not suitable for commercial applications, and its performance may not match that of larger, more resource-intensive models in high-demand scenarios. Nevertheless, Stanford's Alpaca 7B is an excellent model for advancing academic and educational AI, providing powerful tools for knowledge exploration and dissemination.
- Website: [Stanford CRFM Alpaca 7B](https://crfm.stanford.edu/)
- GitHub: [Stanford CRFM GitHub](https://github.com/tatsu-lab/stanford_alpaca)
- License: Non-commercial license
- License Details: [Non-commercial license details](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE)
- Use Cases: Academic research, educational tools, and experimental AI applications.
- Not Suitable For: Commercial applications due to non-commercial license restrictions and potentially less robust performance in high-demand scenarios.
How you can playing and trying these models?
In my opinion one of the most important resource is HuggingFace (https://huggingface.co) where you can find the models (https://huggingface.co/models) the datasets (https://huggingface.co/datasets) and last but not least where to play with these models:
You can also run locally on your system the models: there are various solutions but I suggest LM Studio (https://lmstudio.ai) and Ollama (https://ollama.com). The list is huge and most of these solutions are all great and multi-platforms (x86 and ARM optimized).
You can also run the models via Google Colab: for this scenario I suggest to look at a great guide from TowardsDataScience (https://towardsdatascience.com/llms-for-everyone-running-langchain-and-a-mistralai-7b-model-in-google-colab-246ca94d7c4d).
Last but not least... you can also ask to ChatGPT or Gemini and Claude more information about:
That's enough, don't worry! Just please share your comments, let me know if there are improvements I can make to the document, and share your thoughts! Currently, the model I like the most is Claude from Anthropic, because it has one of the most human-centric approaches to themes such as ethics and governance. However, I hope that one day, the gap could be filled by the others as well!