Today, large language models are an integral part of products like Amazon Alexa, Duolingo, and Google Assistant. Businesses worldwide use their versatility to create chatbots, virtual assistants, inventory management systems, and other applications. LLMs help these products understand, comprehend, and produce text.
Many popular LLM-based tools use open-source models. This means that their code and architecture are available to the public. Developers and LLM researchers can access, improve, and adjust these models to perform various tasks. We’re here to discuss the different capabilities of these models and help enterprises select the best ones.
Table Of Contents:
- Best Open-Source Large Language Models (LLMs)
- How To Choose The Best Open Source LLM
- Best Open-Source ML Model In 2024
- The Most Popular Open Source LLM
- Conclusion
How To Choose The Best Open Source LLM
The market of open-source large language models has a lot to offer. Modern LLMs vary in performance, applications, and enterprise license fees. While most models are accessible for research purposes, this isn’t always the case with enterprise applications. To make selecting LLMs easier for a business.
- Why does the software require LLM? Not every product needs a model to work correctly. Establish what the solution must do and if its work is impossible without an LLM.
- What’s the appropriate level of accuracy? Models like Falcon and LLaMA 2 are among the top open-source LLMs offering enormous amounts of parameters and training data. A business must determine how specific they need the LLM to be.
- How much does the company expect to spend? Likewise, budget is an essential aspect of exploring LLM options. The bigger the model, the more resources and training are involved in working with it. These powerful tools require considerable time and funding to operate correctly, even if they are open-source.
- Does your solution require a pre-trained or a unique LLM? The software works well with open-source pre-trained components for specific scenarios. In rare cases, businesses can invest in fine-tuning models from scratch.
Best Open-Source ML Models In 2024: An Overview
The creation of large language models isn’t an easy task. It requires enormous amounts of information to train and work with different languages. That’s why large corporations almost entirely finance their development. These are the best open-source large language models businesses can integrate into their solutions.
Top 10 Open-source LLMs: A Closer Look
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Google DeepMind introduced the Gemma family of LLMs in February of 2024. They are based on technology and research in making Google’s Gemini models. Gemma LLMs are available in 2 and 7-billion parameter versions and are finetuned using reinforced learning from human feedback and supervised fine-tuning.
Google’s AI developers claim that Gemma models offer the best performance for their size among this class of LLMs. The components can run directly on desktop computers and developer laptops. It’s possible to fine-tune Gemma LLMs with business data for specific applications. They also offer cross-device compatibility for IoT, cloud, mobile, and desktop.
In July 2023, Meta released an updated version of its powerful LLM. It's available in several variants, from 7 to 70 billion parameters. The company fine-tuned Llama 2 using reinforced learning from human feedback. This text-generative model is a powerful chatbot component that performs various natural language tasks, including code generation.
As one of the best open-source LLMs, it comes in different sizes. Businesses have more opportunities to balance performance and usage costs. It’s a popular choice for solutions that require intricate fine-tuning. Llama 2 also has the benefit of being one of the few open-source LLMs that are free for commercial and research purposes.
Mistral AI’s newest LLM has been available since December 2023. It’s a medium-sized model that delivers quality output using only 46.7 billion parameters. Mixtral 8x7B is trained using information extracted from publicly available sources.
The product easily handles a context of 32,000 tokens and produces output in English, French, Italian, German, and Spanish. This makes the LLM an excellent choice for conversational products designed with an international client base in mind. It’s available for commercial use through a license.
Researchers at the Allen Institute for Artificial Intelligence released the open-source model to further the scientific study of LLMs. With OLMo, the institution also released its training data, evaluation codes, and other information. It’s one of the first models to offer open access outside inference codes and sizes.
Allen Institute engineers aim to create a more transparent and collaborative environment for LLM research. OLMo offers higher contextual awareness and a component that reduces the risk of encoding personal data. Being one of the best open-source ML models, OLMo offers 1 and 7-billion parameter versions.
Another recent addition to the open-source LLM market, the Qwen family of models, became available in February 2024. The programmers at Alibaba Cloud offer this model in several sizes: 0.5B, 1.8B, 4B, 7B, 14B, and 72B. Qwen 1.5 is a highly versatile product with English, French, Vietnamese, Korean, Russian, and Indonesian language support.
Alibaba Cloud engineers expanded the capacity of all models to up to 32,000 tokens, trying to meet the increasing demand for long-context comprehension. Qwen 1.5 developers also tried to align its responses with human preferences. They used Direct Policy Optimization and Proximal Policy Optimization techniques to achieve this.
A group of researchers at UC Berkeley developed this LLM using the training data from Openchat 3.5 7B. Its only version uses 7 billion parameters and is trained using reinforcement learning from the AI feedback technique. Starling 7B Alpha is currently among the top open-source LLMs, only slightly less productive than GPT-4 and GPT-4 Turbo.
Enterprises can experiment with and improve the model to their heart’s content. Starling 7B Alpha exhibits admirable results in writing and humanities, faring well against big shots like Claude-1 and GPT-4. However, it falls short in disciplines like coding, math, and STEM, with Zephyr-7B-beta and GPT-4 showing better results.
The Allen Institute for AI made another model that is considered an example of the best open-source LLMs by fine-tuning Meta’s Llama 2. The experts want to make a model capable of handling different user preferences and downstream tasks. Tulu 2 comes in three sizes, using 7 billion, 13 billion, and 70 billion parameters.
Like Qwen 1.5, this model uses Deep Policy Optimization to align its responses with human preferences. Its training data comes from several sources, including samples from GPT-4, Open-Orca, and over 7000 scientific documents. This approach was used to help the model better understand how to summarize, extract information, and fact-check.
It is worth saying that one of the best Microsoft’s research teams was responsible for developing this language model. WizardLM is a fine-tuned variant of Llama available in 7B, 13B, and 70B versions. What makes this model unique is the use of an innovative Evol-Instruct approach. It allows the LLM to generate independent open-domain instructions for various complexity levels.
This helps WizardLM perform better compared to models that use human-generated instructions. It even outperforms ChatGPT in several high-complexity tasks. The main drawback is that the product is only developed for research purposes and not commercial.
YI’s family of LLMs is available in 6B, 9B, and 34B variants. They come in two versions: a base model and a fine-tuned one. Each supports a context window of 200,000 tokens. The developers ensured high-quality input for pre-training and fine-tuning phases through a data-cleaning pipeline.
All Yi models come with various sets of features. They make the LLMs perfect for chat use cases, text-to-image conversations, and bilingual text support. In all cases, enterprises can apply for free commercial licenses. It's possible to explore different options on the developer’s website before choosing the right one.
Huggin Face’s H4 research team developed a family of Zephyr LLMs, and its 7B version is one of the best open-source large language models on the market. The ML model uses an assistant behavior boost to make it more productive in various scenarios. Like Mistral’s LLM, Zephyr 7B offers excellent opportunities for commercial licensing.
Businesses and developers use the powerful language model in different scenarios. It can become a component of conversational AI tools like chatbots and assistants. Zephyr 7B also offers robust applications in research and data analysis, code generation, and language translation.
Conclusion
While the market of close-sourced LLMs is more robust than ever, with major players like OpenAI offering new versions of their products, the open-source scene is just as competitive. Businesses should take their time and go through these options. If they run into trouble, we’re always here to help them make the right choice.
