Microsoft's 14B small model Phi-4 that beats GPT4o is now on Ollama, but I want to discourage you

Recently, Microsoft released a very interesting open source model, Phi-4 ^[1] . It is said that this is a "black technology" with only 14 billion parameters , but it beats GPT-4o. It sounds cool at first glance, but what does it do? What is so special about it?

What is Phi-4?

Let’s look at two sets of pictures first

In short, Phi-4 is an open source language model launched by Microsoft. 14 billion parameters may not sound like the largest in the industry (like GPT-4, which is basically at the 100 billion level), but its goal is to be "exquisite and compact" and focus on performing better in some specific scenarios.

Phi-4’s training data comes from a variety of sources, including:

• • Synthetic datasets
• • Selected public domain website content
• • Academic books and question answering datasets

These data make the model very generalizable, and it has also undergone a super rigorous tuning process :

1. 1.  Supervised Fine-Tuning : Let the model learn to answer questions according to specific instructions;
2. 2.  Direct Preference Optimization : Further improve the relevance and safety of answers.

Finally, Phi-4 supports a context length of 16k tokens , which means it can process about 12,000 English words in a conversation. This is a considerable improvement in small and medium-sized models.

Why did Microsoft create it?

According to Microsoft's official description, Phi-4 is designed to solve the following problems:

1. 1.  Resource-constrained environments
   If your device has limited memory or low computing power, such as in some mobile devices or edge computing scenarios, Phi-4 can still run efficiently.
2. 2.  Scenarios with high requirements for response speed
   Imagine that a user enters a question and you need to return the answer with almost zero latency. This "low latency" requirement is also an area where Phi-4 excels.
3. 3.  Logical reasoning and complex tasks
   Phi-4 can not only chat, but also handle tasks that require logical reasoning or multi-step calculations, such as generating tables and handling complex text analysis tasks.

Can we do something cooler with it?

To be honest, Phi-4 is positioned more like a "general tool brick". It is suitable for building many general-purpose generative AI systems, such as customer service robots, language analysis tools, or lightweight AI auxiliary functions.

However, be careful!
Phi-4 is not a panacea. I am here to pour cold water on you. We must be rational when looking at problems. We cannot only look at its better side. When using it, we must see if we can accept its bad side. Yes, it also has many limitations:

1. 1.  Use with caution in certain high-risk scenarios,
   such as medical diagnosis and financial analysis, which require particularly high accuracy and security. You must be very cautious before using it and do additional testing yourself.
2. 2.  Average performance in non-English scenarios
   Phi-4 mainly supports English scenarios, and its support for other languages ​​is relatively weak. If you have multi-language requirements, you may need to combine other models.

Developers must read: Phi-4 technical details

To help you understand Phi-4 more intuitively, we have summarized some of its key parameters and features, and used a table to compare it with other similar models on the market.

Operating environment recommendations
Microsoft took into account the scenario of limited hardware performance when designing Phi-4, which is very friendly to ordinary developers. The following is the recommended configuration of the model:

{
  "model" : "phi4" ,
"params" : {
    "quantization" : "Q4_K_M" , // Support low-bit quantization
    "context_length" : 16000 , // context length
    "hardware_requirements" : {
      "RAM" : ">= 16GB" ,
      "GPU" : ">= NVIDIA 2060"
    }
}
}

In addition, Phi-4 supports multiple deployment methods, including local operation and cloud API calls, which is very flexible.

What is the actual usage experience like?

Microsoft officially provided some benchmark test results. According to their evaluation, the performance of Phi-4 is very outstanding among models of the same level. It is characterized by high accuracy and fast response speed , and is particularly suitable for tasks that require high context understanding.

However, we also found some problems in the actual test:

1. 1.  Long context processing sometimes degrades
   When the context length exceeds 10k tokens, the model may experience some "memory loss".
2. 2.  Occasionally unstable for complex reasoning tasks.
   For example, in multi-step logical reasoning, if there is fuzzy description in the intermediate process, the model is prone to deviation.

Is Phi-4 worth a try?

If you are a developer looking for a lightweight, low-cost open source large model to build generative AI applications, then Phi-4 is definitely a choice worth trying. Especially in scenarios where computing power is limited or high real-time performance is required, it can bring you great help.

Of course, this model also has limitations, especially in complex reasoning or non-English tasks, and may need to be used in conjunction with other models.

Summary: It’s useful, but don’t count on it.