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.

Llama 2

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:

• Ranked 2nd overall on Hugging Face leaderboard with average score of 67.35
• Comparable performance to GPT-4 but 30x cheaper to run
• Llama 2 Long extends context length to 32,000 tokens, surpassing GPT-3.5 in long context tasks
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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.

Falcon 180B

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:

• Ranked 1st on Hugging Face leaderboard with average score of 68.74
• Comparable performance to proprietary models like PaLM 2 and GPT-4
• Fine-tuned Falcon 180B Chat optimized for conversational AI
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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

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:

• Generates code based on natural language instructions
• Explains how code works line-by-line
• Additional training on 100B Python tokens for Code Llama Python
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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

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:

• Outperforms Llama 2 7B on major benchmark tests
• Approaches performance of Code Llama 7B on code tasks
• Commercially available for real-world deployment
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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

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 13B took only $300 to train
• Scores 6.39 on MT-bench, approaching GPT-3.5
• Larger 33B version available
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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

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:

• 70B version achieves 61% accuracy on 32,000 token QA dataset
• Surpasses comparable models on long context benchmarks
• 16k version effective for real-world tasks up to 16-24k contexts
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For applications like multi-document summarization, Giraffe's expanded context size enables retrieving more relevant information with greater accuracy.

Key Takeaways on the Open-Source AI Ecosystem

The open-source AI selection has expanded at a rapid pace. As the models above illustrate, we're seeing remarkable innovation in:

• Scale - Models like Falcon 180B push size boundaries while smaller models like Mistral 7B and Vicuna maximize efficiency.
• Specialization - Models like Code Llama demonstrate the power of fine-tuning on niche datasets for targeted use cases.
• Accessibility - Open ecosystems enable rapid sharing of models like Llama 2 and Giraffe to accelerate research.
• Affordability - Open models provide high performance at a fraction of the cost of proprietary alternatives.

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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.

Get High-Quality Training Data from Sapien

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.

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To learn more about optimizing your open-source LLM with Sapien's data labeling services, book a demo today.