PHI-3-MINI: AI Model – Careers Ready


by meta Llama 3 Large Language Model (LLM) A few days after unveiling, Microsoft has his 'Lightweight' AI model 'PHI-3-MINI' Unveiled the latest version of.


  • it is believed that This is the first of the three 'small models' Which Microsoft is planning to release.
    • Other two models: PHI-3-Small (7B) and PHI-3-Medium will be available soon.
  • This has helped in areas like language, logic testing, coding and mathematics. It has outperformed similarly sized and next-sized models in various benchmarks.
  • Variant: it two variants Available in, a 4K Context Window with, and second 128K tokens with.
    • this model instruction-tune Are based on. which means that it Have been trained to follow a variety of instructions given by users.
  • possible use
    • Market Providing summaries of longer documents within research reports.
    • Also, marketing and sales departments use it Can be used to write product descriptions or social media posts.
    • It's about products and services Customer chatbot to answer basic questions Can also support.

What are language models?

  • language model AI like ChatGPT, Claude, Gemini etc. Are the main center of applications. These models are trained on existing data to solve common language problems such as text classification, answering questions, text generation, document summarization, etc.
  • Large Language Model (LLM): LLMs are large general-purpose language models, Can be pre-trained and then fine-tuned for specific purposes.
    • 'Large' has two meanings in LLM – Huge size of training data and parameter calculation
      • In the field of machine learning, Where machines are equipped to learn on their own without any instruction, parameter they memory and knowledge are what a machine learns during its model training.
      • They define the skill of the model in solving a specific problem.
  • Small Language Model (SLM): SLM, There are more streamlined versions of the Large Language Model.
    • It also allows for developing and operating smaller AI models compared to LLM. Are cost effective.
    • These are like laptops and smartphones Performs better on smaller devices.
    • SLM on-device and offline'The estimations are very good for resource-constrained environments including scenarios.

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