An open-source LLM is a large language model whose code and design are freely available for anyone to access, use, modify, and share. It is capable of understanding and generating human-like text, answering questions, translating languages, writing content, and summarizing information. Well-known examples include EleutherAI’s GPT-Neo and GPT-J, Stability AI’s StableLM, and BigScience’s BLOOM, which is hosted by Hugging Face.

Because the model’s code and weights are publicly available, enterprises can download, containerize, and run the model on their own infrastructure. When data-sovereignty or compliance rules prohibit sending prompts to a public API, teams deploy the model as a private ai, ensuring that every request and response stays behind the corporate firewall.

Open-source LLMs are part of the wider field of artificial intelligence, specifically, an area known as natural language processing. This branch of AI focuses on how machines interpret and work with human language. Open-source LLMs contribute to this field by giving researchers and developers the freedom to study, customize, and improve models, which helps drive innovation and make advanced language tools more accessible.

They are created through a process known as training, where the model learns language patterns by analyzing large collections of text using powerful algorithms. As a result, the finished model can generate text that feels natural and relevant to the input it receives. It supports a wide range of business applications, such as automating customer service in finance or personalizing online shopping in retail.

How do open-source LLMs work?

Open-source LLMs function in the same way as other large language models. They are trained to generate human-like text by finding patterns in large collections of written content. 

What makes them unique is the fact that they are publicly available. People can access the core parts of the model, including its code and trained weights, and download them for their use. They can also modify or fine-tune the model to better suit their needs.

Here is how open-source LLMs work in practice:

Pretraining on public datasets

Open-source LLMs are trained on diverse and publicly available text sources, including websites, books, code repositories, and research papers. 

During this phase, high-performance computing resources process billions or even trillions of words in order to teach the model about grammar, facts, reasoning patterns, and contextual relationships between words.

Public release of code and weights

Once the model is trained, its components are released for public use. These include:

  • Model weights: The numerical values the model learned during training.
  • Model architecture: The specific neural network design, such as transformer layers (parts that process information in steps to build understanding) and attention mechanisms (methods that help the model focus on important parts of the input).
  • Training data sources or methodology: A summary of the kinds of data that were used, plus how they were filtered (unwanted or low-quality content removed) or tokenized (breaking the text down into smaller words to make it readable for the model).

When components are released under open licenses, developers and researchers can download and run the model on their infrastructure.

Fine-tuning and adaptation

Now that users have access to the base model, they can fine-tune it on their own datasets. The model can be adapted to specific domains such as legal, medical, financial, or retail.

Steps involved in fine-tuning can include supervised learning, which means training the model with labeled examples; reinforcement learning from human feedback (RLHF), where people rank the model’s responses to help it improve; and instruction tuning, which trains the model to follow written prompts and specific tasks better.

Deployment and integration

Teams can either skip the fine-tuning step and deploy the base model or deploy once fine-tuning is complete. 

Deployment involves setting up the model so it can receive input and return output. This usually includes putting the model on a server, using software to load the model’s weights, and setting up an API — a system that allows other software tools to send prompts to the model and receive its responses. 

In many cases, teams use inference frameworks to handle this process. These are software tools designed to simplify and speed up the task of running the model efficiently. Examples include Hugging Face Transformers and vLLM.

Integration means connecting the deployed model to another tool or product. For example, a team might add the model to a chatbot so it can answer customer questions, or use it in a writing tool to help generate summaries or titles.  

Examples of open-source LLMs

There are many different types of open-source LLMs available, as they have been developed for various purposes. What’s more, many businesses are interested in them, as a survey from McKinsey reveals that over 50% of enterprises are using generative AI technology.

  • Meta’s LLaMA series and Falcon 180B are designed to perform well across a wide range of tasks, such as reasoning and content generation. These models serve as a foundation for many community-led projects and derivatives. 
  • BLOOM is known for its training on over 40 natural languages, making it suitable for global content generation and translation. Its transparency in training and data sourcing has also made it a reference point for research.
  • Instruction-tuned models such as Vicuna and FLAN-T5 have been refined to follow prompts more effectively. These are ideal for use cases such as chatbots and digital assistants. 
  • StarCoder was trained on programming-related text to assist with code completion and bug detection.
  • FinGPT is optimized for financial tasks such as summarizing reports or analyzing trends in market data.

Ultimately, the open-source approach supports both broad applications and highly specialized needs.

Open-source LLM use cases  

Open source LLMs can be customized and applied across industries, making them a flexible choice for anyone looking to build or improve AI-powered tools. 

Here are some examples of the ways they are making a difference:

  • Content summarization for research and news: LLMs can process long documents and extract the key ideas, saving time for users who need to review complex information. Using open-source LLMs in this way is particularly helpful for academic, legal, or financial research. For instance, FinGPT, an open-source financial model, helps analysts summarize investor reports and market news.
  • Code assistance and development tools: Software teams use open-source LLMs trained on code to suggest functions, fix bugs, or automate repetitive tasks. The approach saves time and reduces errors during development. Tools like StarCoder have been adopted by engineering teams looking for cost-effective, customizable support when building apps or managing large codebases.
  • Scientific and environmental research: Open-source LLMs are used alongside large data sets to support complex research. NASA and IBM, for example, have explored ways to use open-source AI models with geospatial data to monitor climate patterns and natural disasters.
  • Healthcare support tools: In medicine, open-source LLMs are being tested as assistants for both clinicians and patients. They can summarize medical notes, explain test results, or support training for healthcare professionals. Because the models can be adapted and deployed with secure measures, they are being explored for use in hospitals and clinics.