PHI-3-MINI: AI model

Context 

A few days after Meta unveiled its Llama 3 Large Language Model (LLM), Microsoft  unveiled the latest version of its ‘lightweight’ AI model – the Phi-3-Mini.

Phi-3-Mini

• It is believed to be first among the three ‘small models’ that Microsoft is planning to release. 
  • Other Two Models : Phi-3-small (7B) and Phi-3-Medium will be available shortly.
• It has  outperformed models of the same size and the next size up across a variety of benchmarks, in areas like language, reasoning, coding, and math.
• Variants : It is available in two variants, one with 4K context window, and another with 128K tokens.
  • Theses model is instruction-tuned, which means that it is trained to follow the different types of instructions given by users. 
  • This also means that the model is ‘ready to use out-of-the-box’.
• Potential uses : 
  • Providing summaries of long documents or trends within market research reports. 
  • Also, marketing and sales departments could use it to write product descriptions or social media posts.
  • It could also underpin a customer chatbot to answer basic questions about products and services.

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What are Language Models?

• Language models are the backbone of AI applications like ChatGPT, Claude, Gemini, etc. These models are trained on existing data to solve common language problems such as text classification, answering questions, text generation, document summarisation, etc.
• Large Language Models(LLMs): LLMs are large general-purpose language models that can be pre-trained and then fine-tuned for specific purposes.
  • ‘Large’ in LLMs has two meanings — the enormous size of training data; and the parameter count. 
    • In the field of Machine Learning, where machines are equipped to learn things themselves without being instructed, parameters are the memories and knowledge that a machine has learned during its model training.
    • They define the skill of the model in solving a specific problem.
• Small Language Models(SLMs):  SLMs are more streamlined versions of large language models. 
  • When compared to LLMs, smaller AI models are also cost-effective to develop and operate, and 
  • They perform better on smaller devices like laptops and smartphones.
  • SLMs are great for resource-constrained environments including on-device and offline inference scenarios.

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