Top 8 Open-Source LLMs for 2025 and Their Use Cases

The past decade has seen a surge in cutting-edge smart technologies, most notably the creation and mainstream infiltration of open-source large language models (LLMs).

Large language models present an easy way to search, analyze, summarize, and generate content through natural language processing. These AI models have become essential tools for content generation across various industries. However, every large language model comes with unique features and capabilities, necessitating the need to choose the right one for your specific use case and application specifications — and increasingly, the first fork in that decision is LLM vs. GPT: a paid, general-purpose API from OpenAI, or an open-source model you can run, train, and control yourself.

What Are Open-Source LLMs?

Large language models are AI algorithms that use deep learning techniques and massive datasets of training data to understand, generate, and summarize content. This makes them suitable for various natural language processing tasks, including sentiment analysis, text-to-speech synthesis, text summarization, token summarization, and language translation.

Unlike their proprietary models, which are owned by parent companies and require purchasing licenses, open source models allow anyone to access, modify, distribute, and use them for any purpose. Their public infrastructure makes them more versatile as developer communities constantly optimize them for specific AI applications and data analysis tasks.

Understanding and generating natural language makes LLMs capable of handling a wide range of tasks, including natural language processing, natural language generation, text completion, translation, summarization, and question-answering. They power potential applications in chatbots, virtual assistants, content creation, sentiment analysis, and recommendation systems — essentially any area that involves processing and generating textual data.

Despite the buzz around Generative AI, adoption is still early: according to an Arize AI survey, only around 8.3% of machine learning teams had deployed an LLM application into production as of April 2023. The same survey found that OpenAI’s GPT models currently dominate the field, with 83.0% of ML teams considering or already working with them — though Google, Microsoft, and a growing number of open-source projects are closing the gap.

Open-Source LLMs vs. GPT: How to Choose the Right LLM

Before diving into specific models, it’s worth understanding the fundamental LLM vs. GPT trade-off, since it shapes almost every decision that follows.

Commercial models made by OpenAI are highly popular due to their convenience and availability. OpenAI’s API key is paid based on token usage.

OpenAI’s API and other commercial models typically offer advanced, market-proven capabilities. For those seeking more control and flexibility, open-source large language models are the alternative. Open-source LLMs provide, on the one hand, significantly higher control over the data used to train the model (including the possibility of training on internal data) and, on the other hand, the possibility to change the model architecture itself. Since none of the data is shared with a third party, open-source LLMs also tend to offer stronger security and data-privacy guarantees.

Cutting to the chase — which option is better? As always, “it depends,” and the discussion mirrors the never-ending “buy vs. build” debate in software engineering:

• Paid API LLMs (like GPT) are the go-to choice for teams that want to implement a general-purpose LLM into their workflows instantly, without complex engineering. Time-to-value is minimal, and even underwhelming initial results can be improved by refining prompts. The drawback: the underlying architecture is fixed, just as with any other SaaS solution.
• Open-source LLMs are the better option if you want to know what’s happening “under the hood.” With the right expertise, you can train an open-source model for very niche use cases while keeping full control over the training data — and, as covered below, full control over where sensitive data physically lives.

Why Teams Hesitate to Adopt LLMs at All?

Whichever path an organization leans toward, three barriers consistently come up before any LLM — proprietary or open-source — makes it into production:

• Data privacy — protecting proprietary data is the primary obstacle to widespread LLM deployment. Many ML teams cite this as their top concern, which is one of the main reasons open-source, self-hosted models continue to gain ground.
• Reliability — the accuracy of LLM responses, and the risk of “hallucinations” (generating incorrect or nonsensical information), highlights the need for better LLM observability tools to fine-tune models and troubleshoot issues.
• Costs — implementing and running large language models, whether through a paid API or self-hosted infrastructure, can be expensive, making cost a significant factor in any adoption decision.

GPT And Other Proprietary Models, For Context

To evaluate open-source LLMs fairly, it helps to know what they’re being compared against:

• GPT-3 was released in 2019 and, like its predecessor GPT-2, generates strings of complex language by predicting the next most likely word in a sequence. It was trained on 175 billion parameters, largely scraped from Common Crawl. Multiple GPT-3 versions followed, with GPT-3 Davinci becoming the most stable and popular.
• GPT-3.5 Turbo, released March 1, 2023, combined an improved GPT-3.5 with GPT-3 Davinci. Its key differentiator was Reinforcement Learning from Human Feedback (RLHF) — a method where human raters score the model’s outputs to steer its behavior.
• GPT-4 proved considerably more accurate than GPT-3 and GPT-3.5 Turbo (it was even able to pass the Bar Exam, something GPT-3 Davinci failed). It also introduced built-in multimodality: it can accept prompts combining text and images and generate text outputs — natural language, code, and more — in response.
• LaMDA (Google), like GPT-3 and BERT, is built on the Transformer architecture, which lets a model understand relationships between words in a sentence or paragraph and predict what comes next. Google’s key differentiator for LaMDA was training it specifically on dialogue data, so it could better handle the nuances of open-ended conversation.
• BLOOM (Hugging Face) is a large language model capable of producing coherent, human-like text in 46 languages and 13 programming languages. Architecturally, it’s similar to GPT-3 — an auto-regressive model built for next-token prediction.

Top 8 Open-Source LLMs for 2026

Choosing a reliable open-source LLM isn’t just about picking one with the most parameters – it’s about choosing an LLM suited to your needs that gives you the chance to optimize it further.

1. XLNet

A product of collaborative efforts by Google and researchers at Carnegie Mellon University, offering bidirectional contexts ideal for data science applications. The model comes with a unique autoregressive formulation that enables it to learn bidirectional contexts by maximizing the likelihood over all permutations of the factorization order, thereby following a generalized autoregressive pre-training method.

Essentially, the model offers the best of autoencoding and autoregressive language modeling, while avoiding the limitations of both. Unlike most autoregressive models, XLNet amplifies the expected likelihood of a sequence with respect to all possible permutations of the factorization order.

Thanks to this unique operation, the context of each position can consist of tokens from both the right and left. In anticipation, the model learns to utilize the contextual information of any content from all positions.

XLNet also integrates the relative encoding scheme and segment recurrent mechanism of Transformer-XL into pre-training, thus improving its performance for tasks involving longer text sequences.

2. LLaMA

Developed by Meta AI, this collection of models ranges from 7 to 65 billion parameters, excelling in various tasks including answering questions, language translation, and summarizing text. The open-source LLM is available in various sizes, including 6.7 billion, 13.0 billion, 32.5 billion, and 65.2 billion parameters. Each model excels in different tasks, with the larger models performing better on more complex tasks.

LLaMA is based on the transformer architecture, which has been the standard model architecture for most LLMs since 2018. The open-source LLM shares several similarities with GPT-3 but also comes with a few differences. For instance, instead of the ReLU activation features used in GPT-3, LLaMA uses SwiGLU activation functions.

Similar to other extensive language models, LLaMA operates by receiving a sequence of words as input and predicting the next word, allowing it to generate text recursively. For training, its providers selected text from the 20 languages spoken by the highest number of people, with particular emphasis on languages using Latin and Cyrillic alphabets.

One of the most alluring aspects of the LLaMA models is that they’re trained in diverse domains. This makes them suitable for various applications, including answering questions, translating, summarizing text, and more. They can also be fine-tuned for various AI projects.

3. LLaMA 2

The second iteration is specifically designed for dialogue applications, with improvements including a larger pre-training corpus and doubled context length. As such, the model has undergone extensive fine-tuning to make it comparable to other dialogue models like ChatGPT. Like its predecessor, LLaMA, LLaMA 2 comes in three sizes: 7, 13, and 70 billion parameters, and was trained on 2 trillion tokens.

That said, LLaMA 2 has significant improvements and advancements over its predecessor. For starters, LLaMA 2 is trained on a new set of publicly available data, with a pre-training corpus that’s 40% larger than LLaMA. The context length of LLaMA 2 is also double that of LLaMA, and it utilizes a grouped query attention mechanism, giving it better performance over its predecessor.

LLaMA 2 also has another iteration called LLaMA 2-Chat that is fine-tuned and optimized for chat-based interactions. These further developments ensure that the model’s output is safe and helpful, thus outperforming most mainstream models, including ChatGPT, on human evaluation benchmarks.

The open-source large language model is licensed for researchers and commercial entities, allowing a wide range of users to leverage the model’s capabilities for various purposes, including research, specialized projects, and commercial applications.

What’s even more impressive is the wide availability of training data available for the model. Developers can utilize tokens from publicly available sources and access fine-tuning data from over one million new human-annotated examples and publicly available instruction datasets.

4. Guanaco

Built on Meta’s LLaMA models and fine-tuned using QLoRA, making it more accessible as an AI tool that can run on a single GPU. It is built on the foundation of the LLaMA B model and fine-tuned using the innovative QLoRA (Quantized Low-Rank Adapters) method. QLoRA allows the model to be fine-tuned on a single GPU, making it more accessible to small organizations and private developers.

The QLoRA fine-tuning method also quantifies the model to 4-bit precision and incorporates LoRAS (low-rank adaptive weights), significantly reducing memory requirements and optimizing its performance. This approach allows even the largest 65 billion-parameter Guanaco open-source LLM to function effectively with less than 48 GB of GPU memory, down from 780 GB of GPU memory without compromising performance.

One of Guanaco’s most impressive and distinctive features is its adaptability to extended conversations, with its capability to answer questions or discuss topics upon request, making it especially suitable for chatbot applications.

On the downside, despite its robust performance in natural language processing tasks, the open-source LLM is not licensed for commercial applications. It is primarily intended for academic research and non-commercial applications.

5. Alpaca

Developed by Stanford University researchers with robust instruction-following capabilities for natural language processing tasks and content generation. The model was fine-tuned from Meta AI’s LLaMA 7B model and 52,000 instruction-following demonstrations in the self-instruction style.

This gives Alpaca robust instruction-following capabilities, making it a reliable option for natural language processing tasks that require strict adherence to instructions. This LLM is primarily intended for academic research, but it’s not yet ready for general use due to inadequate safety measures.

It is also not available for commercial use because its instruction data is based on OpenAI’s text-davinci-003, whose terms and conditions prohibit the development of models that compete with OpenAI.

Besides data from OpenAI’s text-davinci-003, the developers also used Hugging Face’s training framework to fine-tune Alpaca, taking advantage of mixed precision training and Fully Sharded Data Parallel.

6. RedPajama

A collaborative effort to create leading open source models with an impressive 1.2 trillion token dataset that helps bridge the quality gap between proprietary and open-source LLMs.

The primary objective of the project is to bridge the quality gap between proprietary and open-source LLMs since most powerful foundational models are currently locked behind commercial APIs, which limit customization, research, and usage with sensitive data.

The RedPajama project consists of three major components including:

RedPajama Dataset

This is an impressive 1.2 trillion token open-source dataset created following the recipe described in the LLaMA paper. This impressive dataset comprises 7 data slices from diverse sources, including C4, GitHub, CommonCrawl, Books, StackExchange, Wikipedia, and arXiv.

Each data slide undergoes meticulous filtering and pre-processing to ensure data quality and token count alignment with the numbers reported by Meta AI in the LLaMA paper.

RedPajama Base Models

RedPajama is made up of two base models: 3 billion and 7 billion parameter models, both developed based on the Pythia architecture. The models also have other variations, including the RedPajama-INCITE-Instruct-3B-v1 and RedPajama-INCITE-Chat-3B-v1.

The RedPajama-INCITE-Chat-3B-v1 model is optimized for conversational AI tasks and is capable of generating human-like content in a conversational context. The RedPajama-INCITE-Instruct-3B-v1, on the other hand, is designed to follow instructions effectively, making it perfectly suited for tasks that require the execution of complex instructions.

RedPajama Instruction Tuning Data and Models

The final component of the triage focuses primarily on fine-tuning the models to excel in specific tasks. It offers variations of the RedPajama-INCITE-Base models, with each variation coming with distinct characteristics and applications.

For instance, the RedPajama-INCITE-Chat models are fine-tuned using Open Assistant and Dolly 2.0 data. Conversely, the RedPajama-INCITE-Instruct models are designed for few-shot prompts, which helps eliminate any dataset that overlaps with the HELM benchmark.

7. Stable Beluga

Models built upon Meta AI’s LLaMA that excel at solving complex problems in specialized fields, making them valuable AI tools for data analysis in domains like mathematics and law. Stable Beluga project consists of two models: Stable Beluga 1 and Stable Beluga 2.

The primary purpose of the project was to bridge the quality gap between proprietary (closed) and open-source LLMs.

In fact, Stable Beluga 2 has outperformed LLaMA 2 in certain benchmarks.

One of the most alluring attributes of the Stable Beluga models is their capability to solve complex problems in specialized fields such as mathematics and law, with a strong focus on fine linguistic details. Since they were released, the models have shown proficiency in responding to complex questions and handling reasoning tasks, making them especially suitable instruments for researchers in specific domains.

Since it’s categorized as a research instrument, Stable Beluga comes under a non-commercial license, further emphasizing the developers’ commitment to promoting research and accessibility in the AI community. The non-commercial license ensures that the models are freely available for academic and other non-commercial purposes, thus encouraging innovation and collaboration in natural language processing.

The Stable Beluga models’ training process is based on the Orca approach, which is quite similar to Microsoft’s progressive learning methodology. That said, the datasets used in the Stable Beluga project aren’t similar to the Orca paper. Instead, the development team utilized Enrico Shippole’s datasets, which included NIV2 Submix Original, COT Submix Original, T0 Submix Original, and FLAN 2021 Submix Original to prompt large language models.

The resulting dataset contained 600,000 high-quality examples, accounting for about 10% of the original Orca data set. Finally, the developers filtered the dataset and removed evaluation benchmarks, resulting in perfectly fine-tuned models with exceptional performance.

8. MPT

A series of transformer-based AI models designed for commercial use, with variations tailored for specific purposes from chatbots to long-form content generation. However, unlike most models designed for commercial use, MPT models are built on the GPT-3 model, giving them greater efficiency and flexibility in various natural language processing tasks.

MPT models come in a number of variations, with MPT-7B-StoryWriter and MPT-7B serving as the two most superior models. The former, MPT-7B Base, is a decoder-only transformer model with 6.7 billion parameters and trained on a corpus of one million tokens of code and text curated by MosaicML’s data team. The base model utilizes AliBi for handling long context lengths and FlashAttention for shorter context lengths.

The model has an Apache 2.0 license but, unfortunately, is not intended for deployment without fine-tuning. Developers also recommend guardrails and user consent for human-facing interactions.

The MPT-7B-StoryWriter-65k+, on the other hand, is a variant of MPT-7B perfectly tailored for reading and writing stories with incredibly long context lengths. The model is a result of fine-tuning MPT-7B on a filtered fiction subset of the books3 dataset, which features 65,000 tokens, enabling the model to generate content as long as 84,000 tokens on a single node of A100-80GB GPUs.

Besides the two variations mentioned above, the MPT model series also includes MPT-7B-Chat and MPT-7B-Instruct, which are fine-tuned for specific purposes. The MPT-7B-Chat is designed to serve as a chatbot-style model for dialogue generation; the model is fine-tuned on multiple datasets, including Alpaca, HC3, Helpful, Harmless, Evol-Instruct, and ShareGPT-Vicuna datasets. However, its CC-By-NC-SA-4.0 license limits its usage to non-commercial purposes.

Conversely, the MPT-7B-Instruct model is tailored for short-form instruction following. The model was created by fine-tuning MPT-7B on a dataset released by MosaicML. The dataset was derived from Databricks Dolly-15k, Harmless, and Anthropic’s Helpful datasets.

MPT-7B’s training process used 8 A100-80GB GPUs with a LION optimizer, shared data parallelism, and the Fully Sharded Data Parallelism (FSDP) technique. Developers also employed a gradient checkpoint to optimize memory usage during training. The model has 6.7 billion parameters, a vocabulary of 50432 words with a sequence length of 65536, 16 attention heads, and 32 transformer layers, each with a hidden size of 4096.

What Are the Benefits of Using Open-Source LLMs?

Despite the benefits of proprietary models, they come at a steep cost. Open-source large language models help overcome these constraints with advantages including:

Transparency and Flexibility

Open Source LLMs provide unmatched transparency by allowing organizations to deploy models within their infrastructure, giving them full control over sensitive data while enabling sophisticated data analysis and AI applications.

One of the greatest hindrances to most proprietary LLMs is the limited power organizations have over the safety of their data. Platforms like GPT-3 cannot properly secure private and corporate data since it’s constantly evaluated by OpenAI’s teams.

Open Source LLMs provide unmatched transparency and flexibility by allowing organizations, even those without a dedicated machine learning team, to deploy the models within the organization’s infrastructure. This gives them full control over their data and ensures that all sensitive information stays within the organization’s network, thus reducing the risk of unauthorized access and data leaks.

Because none of that data has to be shared with a third party, open-source LLMs also tend to offer stronger built-in security measures than a paid API.

Also, unlike closed, proprietary LLMs, open-source LLMs offer transparency regarding how they work, their architecture, training data, methodologies, and how to utilize them effectively.

The ability to inspect code and unmitigated visibility into the algorithm allows the models to gain more trust among organizations, helps ensure ethical and legal compliance, and assists in audits. Optimizing an open-source large language model effectively — through practices like MLOps — can also reduce latency and improve performance.

Cost Savings

Open-source large language models are considerably cheaper than their proprietary counterparts since they don’t require any licensing fees. That said, any organization looking to utilize LLMs must be prepared to shoulder the initial roll-out costs as well as cloud or on-premises computational infrastructure.

Active Community Support

The open-source large language model movement aims to democratize the use and access of LLM and generative AI technologies. One of the best ways to do this is by allowing developers to inspect the inner workings of LLMs to propel future development.

By lowering entry barriers to developers and coders around the world, LLMs can foster innovation and improve existing models by increasing accuracy, reducing biases, and increasing overall performance.

Choosing the Right Open-Source LLM for Your Business

Selecting an open source model depends on your goals, resources, and constraints.

Consider:

• Use Case Fit: Match the model to your specific AI applications
• Infrastructure Requirements: Balance performance needs with available computing resources, or offload that complexity through dedicated AI Integration Services
• License Type: Ensure the model permits your intended use
• Customization Support: Select models with good documentation for easier integration with data science workflows

There are a plethora of open-source and commercial language models deployable on-premise or in a private cloud, which translates to faster business adoption and more robust cybersecurity. As technology evolves, we can expect further developments in LLMs, including multimodal capabilities for understanding and generating diverse data types beyond text, revolutionizing content generation and language translation applications.

Wrapping up

Large language models are poised to change the way institutions conduct research, analyze, and generate content. The development of LLMs will further propel the widespread adaptation and subsequent improvement of LLMs by making them more easily available.

Considering the fact that the technology is only a few years old and can already generate human-like content, we expect to see further developments in the technology, including multimodal capabilities whereby models will be able to understand and generate diverse data types, including text, videos, and images from a unified platform.

Whether you choose an open-source model or a proprietary one, getting real business value out of an LLM rarely comes down to picking the right name off a list — it depends on how well the model is integrated, fine-tuned, and deployed within your existing infrastructure. That’s where working with a team experienced in LLM solutions development makes the difference between a promising proof of concept and a production-ready system that actually moves the needle for your organization.

References

1. Towardsdatascience.com. Introduction to Open Source LLMs. URL: https://towardsdatascience.com/a-gentle-introduction-to-open-source-large-language-models-3643f5ca774. Accessed on Monday 20, 2023
2. Aim research.co.Choosing the Right Open Source LLM: Factors to Consider. URL: https://aimresearch.co/2023/06/13/choosing-the-right-open-source-llm-factors-to-consider/, Accessed on November 20, 2023
3. Arxiv.org. SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling With Backtracking. URL: https://arxiv.org/abs/2306.05426, Accessed on November 20, 2023
4. AI.meta. LLMA. URL: https://ai.meta.com/llama/, Accessed on November 20, 2023
5. Crfm.stanford.edu. Alpaca. URL: https://crfm.stanford.edu/2023/03/13/alpaca.html. Accessed on November 20, 2023
6. Arxiv.org. LLaMA: Open and Efficient Foundation Language Models. URL: https://arxiv.org/abs/2302.13971. Accessed on November 20, 2023
7. Deepleaps.com. Open Source Community Releases Redpajama INCITE AI Models, Surpassing Leading Benchmarks. URL: https://deepleaps.com/news/open-source-community-releases-redpajama-incite-ai-models-surpassing-leading-benchmarks/, Accessed on November 20, 2023
8. Opendatascience.com. Stability AI Has Released Stable Beluga 1 and Stable Beluga 2 New Open-Access LLMs. URL: https://opendatascience.com/stability-ai-has-released-beluga-1-and-stable-beluga-2-new-open-access-llms/. Accessed on November 21, 2023
9. Kdnuggets.com. Introducing MPT7B, a New Opensource LLM. URL: https://www.kdnuggets.com/2023/05/introducing-mpt7b-new-opensource-llm.html. Accessed on November 21, 2023