Artificial Intelligence systems available to the public for free. Any developers can modify, improve, or use them without restrictions. Open source LLMs let researchers and businesses build on existing work. There are many free models. Anyone can use these models, often for non-commercial purposes. In this article, we will discuss best 18 open source LLM Models with example.
What is Large Language Models (LLM Model)
An AI system trains a LLM on vast text data. It understands and generates human-like responses. Examples of LLMs include GPT-4, BERT, and LLaMA. These models vary in size, from millions to billions of parameters.
LLMs provide several benefits, including improved natural language understanding and text generation. They help automate tasks like content creation, translation, and customer service. LLMs enhance productivity and accuracy in various applications. They also enable more human-like interactions in chatbots and virtual assistants. Large language models continue to revolutionize industries with their language processing capabilities.
What is LLM Language Model?
An AI trains a large language model (LLM) to process text. It generates human-like responses by understanding language patterns. An example of an LLM model is GPT-4, which powers ChatGPT.
ChatGPT is an LLM that uses GPT architecture to generate text. The difference between GPT and LLM is scope. GPT generates text based on prompts as a specific type of LLM. LLMs, in general, refer to any LLM trained on vast text data, not just GPT.
What is Open Source Large Language Models?
An open source LLM means the model’s code is available for public use and modification. Open source models are AI systems anyone can access, modify, and distribute freely. There are free LLM available for use. These models allow developers to create applications without licensing costs. Examples of LLMs include Meta’s LLaMA, GPT-3, and BLOOM.
Open source LLMs are often used for research, education, and developing AI-driven tools. They help accelerate innovation by allowing more collaboration and customization within the AI community.
What is LLM Meta AI (LLaMA)?
The Llama model is used for natural language processing tasks. It helps with text generation, translation, and summarization. Meta Llama is designed for AI research and development. It powers applications like chatbots and virtual assistants. A LLM is an AI system trained on vast text data. It understands and generates human-like text based on input.
Llama 3, an upgraded version, is used for more advanced language tasks. It improves accuracy and efficiency in natural language understanding. Businesses use Llama models to automate tasks and improve customer interactions.
LLM Chatbot
LLM in a chatbot refers to a large language model that powers conversation. It helps chatbots understand and generate human-like text. ChatGPT uses an LLM. It relies on this model to answer user questions. The difference between a bot and an LLM is their functionality.
A bot follows programmed rules to perform tasks, while an LLM generates natural responses from vast language data. To turn an LLM into a chatbot, developers combine the LLM with an interface. This interface processes user inputs and outputs conversational responses. The LLM is fine-tuned for specific use cases like customer support or entertainment.
Large Language Models GTP-3
GPT-3 is a free LLM. It has 175 billion parameters, making it one of the largest. ChatGPT, based on GPT-3, is also a LLM. GPT-4 is another LLM with even more parameters than GPT-3. These models are designed for advanced natural language processing tasks. They excel in understanding and generating human-like text. Both GPT-3 and GPT-4 offer impressive capabilities in AI and machine learning.
Difference Between LLM and AI
LLM and generative AI are related but not the same. LLMs are a type of generative AI. ChatGPT is both an LLM and generative AI. AI and ML are different concepts. AI refers to machines simulating human intelligence.
Machine learning (ML) is a subset of AI, where systems learn from data. LLMs are intelligent knowledge models because they process and understand vast information. They predict and generate human-like text. Here are some example ot the difference between LLM and AI:
| Criteria | Large Language Model (LLM) | Artificial Intelligence (AI) |
|---|---|---|
| Definition | A model designed to process and generate human-like text based on language data. | A broad field focused on creating machines that simulate human intelligence. |
| Scope | Specialized in language tasks like text generation, translation, and summarization. | Encompasses multiple fields including vision, speech, robotics, and reasoning. |
| Type | A subset of AI, specifically designed for natural language processing (NLP). | An overarching term that includes LLMs, machine learning, and more. |
| Functionality | Predicts and generates text by analyzing patterns in large datasets. | Can perform a wide range of tasks, including learning, problem-solving, and decision-making. |
| Examples | ChatGPT, GPT-4, BERT, LLaMA 2. | Robotics, computer vision, speech recognition, autonomous systems, chatbots. |
| Training Data | Trained on vast amounts of text data such as books, websites, and documents. | Can be trained on various data types: text, images, audio, or structured data. |
| Use Cases | Chatbots, text generation, translation, content summarization. | Self-driving cars, facial recognition, natural language understanding, gaming AI. |
| Learning Approach | Primarily relies on unsupervised or semi-supervised learning of language data. | Uses different techniques like supervised, unsupervised, or reinforcement learning. |
| Generative Capability | Focuses on generating coherent text based on input. | Can generate images, text, sound, and more depending on the AI type. |
| Goal | Enhance natural language understanding and generation. | Simulate human-like intelligence across multiple domains. |
Best 18 list of Open Source Large Language Models (LLM Models)
An LLM stands for Large Language Model. It is designed to process and generate human-like text. This model can be very large, often with billions of parameters. They learn from vast amounts of data to perform various language tasks. These models are crucial for advanced AI applications. Here are best 18 list of open source LLM Models:
1. Bloom
The BLOOM LLM model is large, with 176 billion parameters. It is designed for multilingual text generation. BLOOM is used in tasks like language translation, summarization, and content generation. The BLOOM language theory focuses on creating a model that understands many languages. It helps improve natural language processing across different cultures. BLOOM supports open research and collaboration in the AI community. By processing vast language data, it provides better results in various language-related tasks.
2. Falcon
Falcon LLM is open source and available for public use. This LLM aims to provide high-quality language processing similar to ChatGPT. However, whether it is better depends on specific use cases and features. Falcon LLM is not always free for commercial use; licensing terms vary.
Falcon 40B is also open source, offering advanced capabilities for natural language tasks. Both Falcon and ChatGPT are powerful tools, but their suitability depends on your needs.
3. Vicuña
Vicuna LLM is an open-source model for AI research and public testing. Users compare and vote on anonymous LLM responses. They can select models to compare and restart rounds. Based on Llama 2, Vicuna-13b is publicly demoed via LMSYS. Fine-tuned with 70K ChatGPT conversations, Vicuna now generates detailed and structured answers, matching the quality of ChatGPT
4. BERT
BERT is an open source large language model (LLM). It is developed by Google. BERT helps in understanding and generating human-like text. There are many open source LLMs available, including BERT and others.
Google BERT is indeed a LLM, designed for natural language processing tasks. It improves tasks like search engine queries and text analysis. Using open source LLMs like BERT allows developers to access advanced language technology for various applications.
5. Llama 2 LLM Model
Llama 2 is a free LLM. It is developed by Meta. Llama 2 is open source, allowing public access and modification. Whether Llama 2 is better than GPT-4 depends on specific use cases and features.
Llama 2 is free for research and non-commercial use. However, commercial use may require a license. Both Llama 2 and GPT-4 offer advanced capabilities in natural language processing.
6. Gpt-neox-20b
GPT NeoX 20B is free to use for certain purposes. It is an open source LLM. GPT NeoX 20B is a powerful version with 20 billion parameters. It is designed for advanced text generation and understanding tasks. As an open source LLM, GPT NeoX allows developers to modify and use it in various applications. Its large size enables high-quality performance in natural language processing tasks.
7. MPT Open Source Large Language Model
MPT-7B, or Mosaic Permissive Transformer, is an efficient and scalable model trained on one trillion tokens. It performs complex NLP tasks with minimal resources. MPT-7B comes in specialized versions for instructions and dialogue. It was trained on the MosaicML platform in 9.5 days at ~$200k, and is open source. This model competes with LLaMA-7B and represents the growing trend of open-source large language models.
8. Dolly LLM Model
The open-source Dolly 2.0 LLM is designed for instruction-following tasks. It runs on Paperspace Gradient Notebooks using Graphcore IPUs. The model, along with its weights, source code, and dataset, is available under an open-source license, making it suitable for commercial use.
Databricks fine-tuned Dolly 2.0 with data from over 5,000 employees, enabling text summarization and question answering. It contrasts with closed models like ChatGPT by being open and customizable.
9. Opt-175b
OPT-175B is an open source model developed by Meta. It is designed for advanced natural language processing tasks. OPT-175B has 175 billion parameters, enabling it to handle complex text generation and understanding. The model is used in various applications like chatbots and content creation.
OPT is used to enhance AI capabilities in understanding and generating text. There are different types of OPT models, including smaller versions like OPT-125M and larger versions like OPT-175B.
10. Xgen-7b
Salesforce’s XGen-7B is a language model trained on extensive data to generate human-like text. Released under the Apache-2.0 license, it is available for both research and commercial use. Despite its 7 billion parameters, XGen-7B outperforms larger models, offering strong performance with an 8K context window in its most advanced variant. It emphasizes efficiency, using fewer parameters than models like LLaMA 2 or Falcon. The XGen-7B-{4K,8K}-inst variant, trained on instructional data, is available only under a non-commercial license.
11. Mistral AI
The Mistral model is open source. You can access and use Mistral AI for free. Mistral is indeed a large language model (LLM). It is designed to handle complex text generation and understanding tasks. Mistral 7B is a specific version of the LLM with 7 billion parameters. It offers advanced capabilities for natural language processing. By being open source and free, Mistral allows developers to integrate its AI features into various applications.
12. T5
The T5 model is open source and widely available. T5, or Text-To-Text Transfer Transformer, is a versatile NLP model. It treats all tasks as text-to-text problems, which simplifies training. T5 technology leverages transformer architecture for improved performance.
Compared to BERT, T5 often offers better results in diverse tasks. While BERT focuses on understanding context within text, T5 converts all inputs into text and generates outputs accordingly. This approach allows T5 to handle a broader range of NLP tasks more effectively. Overall, T5 provides robust capabilities and is a valuable tool for various applications.
13. Alpaca
The Alpaca LLM is a free and open-source model that generates and understands text efficiently. Building on the LLaMA model, Alpaca focuses specifically on instruction-following tasks. Unlike LLaMA, which serves general purposes, Alpaca generates responses from given instructions. While both models share similarities, Alpaca is tailored for specialized tasks.
14. CodeGen Open Source Large Language Model
The CodeGen LLM is a language model designed to generate code. It helps in writing and understanding code more efficiently. The CodeGen model is part of Salesforce’s suite of tools. Salesforce CodeGen is open source, making it accessible for various uses. It provides robust code generation capabilities for developers.
The CodeGen tool simplifies coding tasks by automating code generation. By leveraging this model, developers can enhance productivity and reduce errors. CodeGen supports multiple programming languages and integrates easily into development workflows. Overall, it streamlines the coding process and improves code quality.
15. GPT-3
GPT-3 is indeed a large language model (LLM). It powers many advanced AI applications. ChatGPT uses LLM technology to generate human-like responses. GPT-3.0 is not free; it requires a subscription for full access. While chatbots can use LLMs, not all chatbots rely on them.
LLMs help chatbots understand and respond better. They improve the interaction quality and accuracy. GPT-3 enhances various applications with its LLM capabilities. It plays a crucial role in AI-driven conversations and content generation.
16. Llama by Meta
The Llama model offers open-source access, and Meta Llama 3 is free to use. Llama 3.1 also offers open-source access. Llama models target natural language processing tasks and differ from GPT models primarily in their architecture and training data. While Llama emphasizes multilingual capabilities, GPT models excel in extensive text generation abilities.
17. PaLM API
The PaLM API is not free; it requires a subscription or usage fee. There are many open source LLMs available. Google developed PaLM as a proprietary model, and it is not open source. Google designed PaLM as an LLM to advance text understanding and generation.
18. Pythia
Pythia is a series of LLMs. It includes various model sizes for flexibility. Pythia AI helps in natural language processing and generation. The models range from smaller versions to more extensive ones.
Pythia aims to advance AI capabilities in understanding and producing text. It contributes to research in machine learning and AI. Pythia models offer tools for diverse applications, from chatbots to complex data analysis. Their scalability makes them suitable for different tasks and industries. Pythia has enhanced the field of AI by providing robust and versatile language models.
What is Large Language Model Training?
LLM training is the process of teaching AI to understand language. These models learn from vast amounts of data. Training involves feeding the model text data and adjusting it over time. The model learns patterns in the data to predict words and phrases.
LLM use diverse text sources for training. This can include books, websites, and other written content. The examples used to teach the model are the training data for machine learning (ML) models. It includes text, images, or numbers that help the model learn. The cost to train a language model depends on its size.
Training of large models can cost millions of dollars. It involves expensive hardware and energy costs. Most organizations train LLMs on cloud computing platforms. These platforms charge based on resources and time used for training.
Final Thought
Open source large language models (LLM Models) means the model’s code is publicly accessible. The community can change, share, and reuse it. The largest open-source LLM is Meta’s LLaMA 2, released in 2023. It comes with models up to 70 billion parameters.
Nasir H is a business consultant and researcher of Artificial Intelligence. He has completed his bachelor’s and master’s degree in Management Information Systems. Moreover, the writer is 15 years of experienced writer and content developer on different technology topics. He loves to read, write and teach critical technological applications in an easier way. Follow the writer to learn the new technology trends like AI, ML, DL, NPL, and BI.