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In the coming year of 2025, generative artificial intelligence (GenAI) is expected to reach a critical turning point, where we will gain a clearer understanding of its true potential. As analyses of its various applications across industries deepen, the GenAI that was once shrouded in noise will gradually reveal its true nature. Recently, Hieb noted that businesses investing in GenAI do not necessarily have to choose expensive and complex solutions. In fact, more and more companies are discovering that large language models (LLMs) don't always...

Recently, Zyphra officially launched Zamba2-7B, a small language model with unprecedented performance, featuring 7B parameters. This model claims to surpass its current competitors in quality and speed, including Mistral-7B, Google's Gemma-7B, and Meta's Llama3-8B. The design goal of Zamba2-7B is to meet the needs of environments that require strong language processing capabilities but are constrained by hardware conditions, such as processing or using it on devices.

Recently, Microsoft announced that its AI Vice President, Sebastian Bubeck, is leaving the company to join OpenAI. This marks a significant career shift for the seasoned employee who has worked at Microsoft for a full decade. Image source notice: the image is generated by AI, licensed from the service provider Midjourney. During his time at Microsoft, Bubeck was primarily responsible for developing small language models. Small language models are a technology aimed at balancing efficiency and effectiveness, with the goal of achieving performance similar to that of larger models without sacrificing performance.

Nvidia's research team has successfully launched Llama-3.1-Minitron4B using model pruning and distillation techniques. This is a compressed version of the Llama3 model, aimed at implementing artificial intelligence on devices. The model reduces the parameter count of the original 8B model through deep and width pruning techniques while maintaining performance close to larger models. Despite a significant reduction in training data (by 40 times), the model achieved a 16% performance improvement on the MMLU benchmark.

Zyphra has launched the Zamba2-2.7B language model, which is a milestone in the small language model domain. Its performance and efficiency have significantly improved, with a training dataset size of around 30 trillion tokens, reducing resource requirements during inference and making it an efficient solution for mobile applications. Key highlights include a twofold increase in response generation speed, a 27% reduction in memory usage, and a 1.29 times decrease in generation latency, particularly suited for applications requiring real-time interaction, such as virtual assistants and chatbots.

Microsoft has announced the launch of serverless fine-tuning capabilities for the Phi-3 small language model, enabling developers to easily adjust model performance on the Azure AI platform without managing underlying infrastructure, and it is free for initial use. The Phi-3 model, with 3 billion parameters, is suitable for enterprise developers, offering efficient performance at a lower cost. Although it has fewer parameters compared to OpenAI's GPT-3.5, it performs similarly across various applications. Serverless fine-tuning simplifies operations, reduces barriers, and supports the fine-tuning of small to medium models.

Recently, NVIDIA launched the Minitron series of small language models, including 4B and 8B versions, significantly increasing training speed by 40 times while greatly reducing resource and data requirements, resulting in cost savings. By combining techniques of 'pruning' and 'knowledge distillation', the Minitron models maintain performance while reducing size, allowing developers to harness advanced technology for applications such as translation, sentiment analysis, and dialog AI at a lower cost. The open-source nature of the Minitron models enables more people to easily access and use them.

2023 has been one of the most disruptive years in the field of artificial intelligence, with a plethora of generative AI products entering the mainstream. Continuing its transformative journey, generative AI is expected to transition from an exciting topic to real-world applications in 2024. The field of generative AI is rapidly evolving, giving rise to a series of widespread trends that will facilitate the adoption of AI across various industries and its presence in our daily lives. Generative AI models are transcending text creation through the integration of multimodal functionalities.