Best 10 Open Source Large Language Models (LLMs)

Technology | 20-01-2025 | Anand Subramanian

large language models

Large Language Models (LLMs) as the name suggests are large sets of data in bulk that function on artificial intelligence (AI) and machine learning programs to understand characters, words, sentences, or complex/unstructured data without human intervention. These language models are flexible and open to fine-tuning the existing data, for instance, understanding the questions, generating relevant answers, or translating language text.

Here is How to Choose an Open-source LLM

Choosing an open-source large language model (LLM) can be an overwhelming task, with several options available today:

  • Identify Use Case: Identifying the usage of freely sourced tools should be clear to enter specifics and generate relevant results through generative AI development services. This simplifies the process and also helps with clarity in the selection process.
  • Evaluate Model Performance: Look into various open-source LLMs and assess their performance metrics. Research benchmarks, user reviews, and community feedback to understand how well the models perform on tasks similar to yours.
  • Check Licensing and Community Support: Ensure the open-source LLM you consider has a permissive license that aligns with your project needs. Also, evaluate the community around the model; strong community support can indicate better maintenance, more resources, and additional help during implementation.
  • Consider Scalability and Resource Requirements: Analyze the computational resources needed to run and fine-tune the LLM. Some models require substantial hardware and memory, while others may be optimized for lower-resource environments, so choose according to your infrastructure capabilities.
  • Review Documentation and Ease of Integration: Good documentation is crucial for successful implementation. Examine the available tutorials, API references, and how easy it is to integrate the model into your existing systems to minimize onboarding time.

Benefits of Using Open-Source LLMs

The research and development community is becoming more and more interested in open-source large language models (LLMs) due to their many benefits. By enabling a wider range of applications across industries, the use of open-source LLMs promotes the culture of knowledge sharing and ultimately advances artificial intelligence as a whole. The following are the main advantages of utilizing open-source LLMs:

  • Cost-effectiveness: Because open-source large language models (LLMs) are usually free to use, they greatly lower the cost barrier for people and businesses wishing to adopt cutting-edge AI technology without having to pay expensive license costs.
  • Customization and Flexibility: Open-source models can be altered and modified by users to suit particular use cases, enabling customized solutions that satisfy particular corporate needs or research objectives and improving overall efficacy.
  • Transparency: Improved comprehension of AI results in open-source LLMs' full insight into the underlying algorithms and training data, which is essential for ethical and legal reasons.
  • Collaboration: Being a member of the open-source ecosystem, supports developers, researchers, and practitioners to work together, which promotes creative breakthroughs and knowledge sharing to a great extent.
  • Constant Improvement: Regular community contributions help open-source LLMs maintain regular updates, bug corrections, and improvements. This keeps the models' performance and relevance throughout time.

What is Multi-model LLM?

An advanced computational framework that combines several models or modalities to improve its comprehension and generation of language is known as a multi-model large language model (multi-model LLM). The system can process and interpret a variety of information types thanks to these models, which can incorporate text, images, audio, and even structured data inputs.

Using a multi-model approach has the advantage of utilizing the complementary strengths of various data types. For example, combining textual and visual information can result in richer contextual responses and more nuanced interpretations. The creation of more cohesive and contextually aware outputs across multiple domains is made possible by this innovation, which is especially advantageous in applications like content creation, interactive AI systems, and advanced data analysis.

Free LLM Model Tools Available Today

Businesses have widely accepted artificial intelligence as a technology. Powerful large language models (LLMs) form the basis of AI-generated tools, which are available as licensed and open-source models. Start-ups and medium-sized organizations primarily rely on open-source LLMs to match their budget needs. Here are some top LLM tools freely available today:

  • Llama 3.1: Llama 3.1 is an innovation in artificial intelligence, supporting conversational capabilities and providing users with an intuitive experience. With its advanced natural language processing abilities, this iteration surpasses its predecessors by refining contextual understanding and delivering responses that feel remarkably human-like. The improvements in Llama 3.1 engage in richer dialogues and offer more personalized interactions.
  • BERT: BERT is a highly effective tool for a range of natural language processing tasks, including entity recognition, sentiment analysis, and question-answering. One of BERT's main advances is its bidirectional training mechanism. This feature makes it possible for BERT to attain relevant results on a wide range of tasks and benchmarks, securing its place as a fundamental model for numerous AI applications.
  • LLaMA2: The efficiency and scalability of this LLaMA-based model have been significantly improved. It is ideal for applications that need to process large volumes of text data because of its design, which concentrates on large-scale language understanding tasks.
  • BLOOM: This large-scale language model works with an innovative approach to understanding and generating human-like text. By harnessing a diverse range of training data, BLOOM can produce coherent, contextually relevant responses, making it a valuable tool for applications in content creation, customer service, and education. Its open-source nature encourages collaboration and further development, reflecting a growing trend towards transparency and accessibility in AI research.
  • GPT-J: GPT-J is one of the most powerful open-source language models available. It can perform a variety of natural language processing tasks, including text generation, summarization, translation, and question answering, with a significant degree of fluency and coherence.
  • Vicuna 13-B: Vicuna 13-B increases contextual awareness and coherence in producing human-like text. This model leverages the latest machine learning techniques, incorporating extensive training datasets to refine its performance across diverse applications, ranging from conversational agents to content creation tools. The development of Vicuna 13-B reflects a significant evolution in AI technology, aiming to meet the growing demands for intelligent and responsive systems in various sectors including customer service, education, and entertainment.
  • GROK AI: Grok AI open-source LLM uses sophisticated natural language processing techniques to transform text summary and understanding. It uses sophisticated natural language processing (NLP) algorithms to precisely extract important insights from intricate documents.
  • XL Net: A generalized autoregressive pretraining method is the foundation of the open-source XLNet Large Language Model (LLM). XLNet presents a permutation-based pretraining technique designed to overcome the drawbacks of conventional autoregressive models. This enhances language interpretation and generating capabilities by enabling XLNet to model dependencies beyond neighboring words.
  • OPT-175B: This open-source model focuses on optimization techniques to boost large-scale text data management's performance and speed. OPT-175B can accurately create and interpret language because it is based on a transformer architecture.
  • XGen-7B: An open-source large language model (LLM) called XGen-7 B was created for challenging text-generation applications. Because it generates engaging language that sounds like human writing, this model is suitable for applications that need the development of original content. XGen-7B can understand intricate linguistic patterns and nuances because it is based on transformer architectures.

What's the difference between Open source vs. closed source LLMs?

A significant difference between open-source and closed-source large language models (LLMs) lies in their accessibility and usage rights. Open-source LLMs are openly available, allowing anyone to inspect, modify, and distribute the underlying code and data. This fosters a collaborative environment where developers and researchers can contribute to enhancements, spur innovation, and customize models to specific needs.

In contrast, closed-source LLMs are proprietary, meaning their source code is not publicly accessible. Users typically must abide by licenses that restrict usage, modification, and distribution. This model often prioritizes commercial interests, as companies may seek to protect their intellectual property and ensure a competitive advantage. While closed-source LLMs might offer robust features and support from the organization behind them, they can also lead to concerns regarding opacity, ethical considerations in AI usage, and potential biases that may not be evident due to the lack of transparency.

Disadvantage of Open Source LLM

Large language models (LLMs) that are open-source have drawbacks despite their many benefits, which include community cooperation, transparency, and accessibility. The possibility of abuse is a major worry since these models' open availability can make it easier for biased or damaging content to be produced. Individuals or organizations may use these technologies maliciously, such as for harassment, disinformation, or other immoral activities, if there is little regulation or control.

The viability of the project may also be hampered by the resource-intensive nature of maintaining and improving open-source LLMs, which frequently call for knowledge and money that are not easily available to all contributors. Furthermore, because open-source development is collaborative, output quality and dependability can vary greatly, resulting in inconsistent results and performance issues in specialized applications.

To sum up, Open Source Large Language Models are a major development in the fields of artificial intelligence and natural language processing. These models encourage creativity and cooperation among researchers, developers, and companies by making strong language processing tools more widely available.

Open-source frameworks' versatility and adaptability allow a wide range of users to create and modify apps to suit different requirements, which eventually improves the potential of AI implementations in numerous industries. To guarantee responsible development and application, it is crucial to address the ethical issues surrounding their use, such as potential biases and misuse. Achieving a balance between transparency and accountability will be essential as the sector develops to fully utilize these game-changing innovations.

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Author

Anand Subramanian

Anand Subramanian is a technology expert and AI enthusiast currently leading the marketing function at Intellectyx, a Data, Digital, and AI solutions provider with over a decade of experience working with enterprises and government departments.