4 Best Open-Source LLMs for Your Business in 2024

As large language models (LLMs) continue to captivate the world with their remarkable capabilities, a parallel movement has been gaining momentum – the rise of open-source alternatives.

While industry giants like OpenAI, Anthropic and Google bask in the limelight with their proprietary models such as ChatGPT, Claude, and Gemini, a growing community of researchers and developers have embraced the open-source philosophy, driven by a commitment to transparency, accessibility, reproducibility and collaborative innovation.

A few weeks ago, the push to democratize LLMs gained further traction when Elon Musk, one of OpenAI’s six co-founders, sued the company for deviating from its initial open-source agreement. To walk his talk, Must announced that his AI startup, xAI will open-source its chatbot, Grok.

Whether Musk wins that case or not, one thing is certain: it won’t stop open-source LLMs from offering a compelling proposition for businesses seeking to leverage the power of generative AI without the constraints of exorbitant licensing fees or opaque data practices.

Some argue that harnessing the collective brainpower of the open-source community could make open-source LLMs more potent than their closed-source counterparts in specific domains.

A recent poll suggests that open-source LLMs will outperform commercial versions in the next two years. A research paper measuring the capabilities of open-source LLMs against paid versions also found that open-source LLMs demonstrate competitive potential against ChatGPT in specific tasks.

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Key Takeaways

  • Open-source LLMs like Falcon180B, Llama 2, Mixtral AI, and Smaug-72B are gaining momentum, driven by a commitment to transparency, accessibility, and collaborative innovation.
  • While open-source LLMs are more affordable, they still require initial setup investments, and their performance may lag behind proprietary models like GPT-4 and Claude 3 in certain tasks.
  • Open-source LLMs have the potential to outperform commercial versions in specific domains or tasks through fine-tuning and adaptation by the developer community.
  • Although experts differ on whether open-source LLMs will surpass proprietary models in overall performance, their rapid progress and the collective brainpower of the open-source community suggest they will continue to close the gap.
  • Open-source LLMs are well-positioned to excel in domain-specific applications through fine-tuning, while proprietary models may retain an advantage in general-purpose applications due to their access to vast, high-quality training data.

Why Open-Source LLMs Are Gaining Wider Appeal

Apart from a capability standpoint, proprietary LLMs have been shrouded in controversies as to how they train their models and the actual amount of data fed to them. It’s fair to argue that if these models were trained with publicly available datasets, why then are they being commercialized? Some of these controversies have led to many lawsuits and the outright ban of ChatGPT in Italy last year due to privacy concerns.

Again, the open-source approach also fosters a culture of experimentation and iteration and, as such, can empower businesses to fine-tune and adapt these models to their specific use cases, unlocking new possibilities and enabling more nuanced and contextual applications.

From a financial perspective, open-source LLMs like Meta’s Llama 2 and Falcon 180B are more affordable as they do not require licensing fees. This presents an enticing option for organizations with budgetary constraints. However, DeepChecks points out that while they are affordable, open-source LLMs still require some form of initial setup investments.

4 Best Open-Source Large Lanuage Models for Businesses in 2024

While there are scores of open-source LLMs, our attention will be on these four due to their proven capability and established status in the open-source community.

4. Falcon180B

Developed by the Technology Innovation Institute (TII) of the United Arab Emirates, Falcon180B is arguably the biggest open-source LLM to launch in 2023. TII says the model is trained on 180 billion parameters and 3.5 trillion tokens from a RefinedWeb dataset.

TII claims the model performs exceptionally well in tasks like reasoning, coding, proficiency, and knowledge tests, and has already outperformed Llama 2 and GPT-3.5 in various natural language processing (NLP) tasks. TII also boasts that the LLM can go toe-to-toe with Google’s PaLM 2, the LLM that powers Google Bard.

As of October 2023, Hugging Face, one of the leading platforms for NLP research, ranked Falcon180B number one for pre-trained language models on some metrics.

3. LLAMA 2

Meta-owned Llama 2 is another quality freely accessible LLM. Llama 2 is available to a wide range of users, from individual enthusiasts to professional researchers and businesses.

Publicly released in 2023, the Llama 2 family of LLMs is a collection of pre-trained and fine-tuned generative text–based AI models ranging in scale from 7 billion to 70 billion parameters. Llama is offered in 7, 13, and 70 billion parameter sizes.

The LLM also offers a fine–tuned model known as Llama-2-Chat that is optimized for dialogue use cases only. According to Huggingface, “Llama-2-Chat models outperform open-source chat models on most benchmarks we tested.” Human evaluation tests for helpfulness and safety revealed that the Llama-2-Chat models are on par with ChatGPT and PaLM.

2. MIXTRAL AI

Mistral is a startup founded by researchers formerly associated with tech giants Meta and Google. They made a foray into the open-source gen AI community last year with their 7-billion-parameter LLM. According to the Paris-based company, Mistral 7B outperforms other prominent open-source LLMs like LLaMA 2 on numerous metrics. In December 2023, Mistral generated significant buzz by releasing an even more capable model called Mixtral 8x7B via a torrent link, demonstrating their commitment to open source.

Mixtral 8x7B is licensed under the Apache 2.0 license and has undergone a series of benchmark testing under Huggingface. Given its lightweight but enhanced performance, Mixtral 8x7B stands as one of the best LLMs in terms of cost/performance trade-offs.

1. Smaug-72B

Developed by Abacus AI, Smaug-72B made an entry into the LLM community last month. Evaluation report on the Hugging Face Open LLM leaderboard shows that Smaug-72B is the first and only open-source model to achieve an average score exceeding 80 across all major LLM evaluations.

According to Abacus AI, Smaug-72B was fine-tuned using existing datasets from “Qwen-72B,” a robust language model introduced a few months prior by Qwen, a research team affiliated with Alibaba Group.

While it still falls short of the 90-100 point average mark regarded as human-level performance, its release suggests that open-source AI may soon rival the capabilities of models developed by private companies.

Will Open-Source LLM Overthrow Private LLMs?

Despite their achievements so far, Alan Smithson, CEO at MetaVRs believes that open-source LLMs will not outpace proprietary LLMs in performance, research and development.

In a chat with Techopedia, he said:

“Open source is a movement that cannot be stopped. The world has benefitted greatly from software built from a network of engineers rather than one organization.

 

“However, open-source LLMs will always be a step behind private companies because the economics allow for much more focused R&D. Sometimes with open-source projects, a main contributor leaves causing delays to the progress of development.”

In terms of performance, Smithson argues that it is unlikely that open-source models outpace those like GPT4/Claude3, etc in tasks but can come close.

“If provided with enough data and compute power, combined with a dedicated team leading the charge, it is feasible that open source could perform certain tasks better, but unlikely.”

For Julien Salinas, Founder and CEO at NLP Cloud, it’s still difficult to predict the exact trajectory of open-source LLMs compared to proprietary models like GPT-4 or Claude 3.

“I personally think that open-source LLMs can beat proprietary LLMs in the coming years. However, this will likely depend on a combination of factors, such as data availability, AI research advancements, and continued collaboration and investment in the open-source community.”

For Marie Maria Sukhareva, Senior AI Expert at Siemens, open-source LLMs will have a very hard time surpassing GPT-4 and Claude-3.

She said:

“OpenAI has created for itself an incredible competitive advantage once they launched their public website. Through that website used by millions of people on a daily basis, they managed to collect the most representative dataset in history which they use for improving their models. Google is trying to do the same with the public access to Gemini.”

Sukhareva also argued that since most open-source LLMs are trained from synthetic data generated by GPT-4, their performance will always be limited. However, she believes that open-source LLMs could have an upper hand in domain-specific performances if rightly fine-tuned.

“I see the hope for open-source models rather in the area of applications that are very domain specific, for example, PLC programming in Siemens, that would need fine-tuning and where GPT-4 will not fare well. I do not think that any time soon open-source models will be able to compete with proprietary model-generalists for general domain applications e.g. FAQ bots, first-level support, etc,” she added.

The Bottom Line

While proprietary models may currently retain a slight performance and userbase advantage, the rapid progress of open-source large language models is undeniable, and they are swiftly closing the gap.

Remarkably, some open-source LLMs have already demonstrated superior capabilities compared to their larger-parameter counterparts, underscoring the impact of high-quality training data, which can outweigh sheer model size.

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Franklin Okeke
Technology Journalist

Franklin is an author and tech journalist with over seven years of IT experience. Coming from a software development background, his expertise lies in all things cybersecurity, AI, cloud computing, and IoT. Apart from Techopedia, Franklin’s work has been featured in many tech publications such as TechRepublic, The Register, TechInformed, and Moonlock.  In addition to pursuing a Master's degree in Cybersecurity & Human Factors from Bournemouth University, he has two published books and four academic papers to his name.  When he is not reading or writing, Franklin either trains at a boxing gym or plays the piano.