The open-source AI ecosystem has expanded rapidly in 2023, with exciting new large language models (LLMs) emerging that can rival proprietary models like GPT-4. As we look ahead to 2024, here are 6 open-source LLMs that are poised to drive further innovation in AI.
Released by Meta and Microsoft in 2022, Llama 2 is arguably the most versatile and high-performing open-source LLM available today. With up to 70 billion parameters trained on over 2 trillion tokens, Llama 2 excels at natural language processing tasks like reasoning, summarization, and knowledge tests.
Key highlights:
Llama 2 strikes an optimal balance between power, cost, and commercial viability for real-world NLP applications. The model represents the cutting edge of open-source AI capabilities.
With 180 billion parameters trained on 3.5 trillion tokens, Falcon 180B from the UAE's Technology Innovation Institute is currently the top-ranked open LLM. It achieves state-of-the-art results in reasoning, coding, and knowledge tests.
Key highlights:
Despite its power, Falcon 180B has restrictive licensing terms for commercial use. But for researchers, it offers unmatched access to experiment with an ultra-large open LLM. Falcon 180B pushes the boundaries of what's possible with open-source AI.
Code Llama from Meta focuses squarely on code generation and explanation. Fine-tuned on 500 billion tokens of code, it writes and describes code in languages like Python, Java, C++, and more.
Key highlights:
For developers, Code Llama supercharges productivity by automating coding tasks. It also helps novice coders better understand programming concepts through its unique explanatory abilities.
Mistral 7B packs impressive performance into its efficient 7 billion parameter size. Leveraging innovations like grouped-query attention and sliding window attention, it processes text rapidly while keeping costs low.
Key highlights:
For scaled-down NLP applications, Mistral 7B delivers outstanding value. Its balance of small size and high performance makes it an appealing choice over larger models.
Vicuna from UC Berkeley achieves ~90% of ChatGPT quality but with a fraction of the parameters. Fine-tuning Llama 2 on 70,000 real conversations gives Vicuna strong conversational abilities.
Key highlights:
Vicuna hits a sweet spot between cost, size, and conversational quality. For many real-world chatbot use cases, it may offer the optimal combination of features.
Giraffe from Abacus.AI extends the context length of Llama 2 to 32,000 tokens, enabling stronger performance on tasks requiring long-term reasoning.
Key highlights:
For applications like multi-document summarization, Giraffe's expanded context size enables retrieving more relevant information with greater accuracy.
The open-source AI selection has expanded at a rapid pace. As the models above illustrate, we're seeing remarkable innovation in:
These factors are combining to make open-source AI a disruptive force in the industry. As the ecosystem matures further in 2024, we can expect open models to become even more competitive with closed counterparts.
The democratization and decentralization of AI research is ultimately a big win for innovation. We're only beginning to tap into the potential of open-source AI, and the future looks incredibly bright.
As we've explored, large language models like Llama 2 and Vicuna achieve remarkable performance through massive datasets and fine-tuning. But clean, accurate, and diverse training data and data labeling is critical for realizing the potential of open-source LLMs.
That's where Sapien comes in.
Sapien provides high-quality data labeling to fuel the next generation of open-source AI. Our global team of subject matter experts meticulously labels datasets for machine learning across all industries.
Whether you need text, image, video, or speech data labeled, Sapien brings precision and scale through a combination of human insight and data ops automation.
To learn more about optimizing your open-source LLM with Sapien's data labeling services, book a demo today.