Phi-4-mini-instruct

Phi-4-mini-instruct

A 3.8B open instruction model from Microsoft with a 128K token context, built for reasoning, math and logic in tight memory or latency budgets. It suits developers adding text features to apps.

Made by microsoft on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

About

What it is: Phi-4-mini-instruct is a small open instruction model from Microsoft, with 3.8 billion parameters, in the Phi-4 family. It was trained on synthetic data and filtered public web pages chosen for quality and reasoning, then refined with supervised fine-tuning and direct preference optimization. It handles a context of 128K tokens. This release also improves instruction following and function calling over the Phi-3 series.

Who it suits: It suits developers who build text features where memory, compute or response time is limited. The card names strong reasoning, especially math and logic, as one of its target jobs.

Title: Who might use it
Industry | Who uses it | How they use it | Example
Software | App developers | Add an assistant that must run in little memory | Answer user questions inside a small server or edge device
Education | Learning tool builders | Build math and logic help | Return a worked explanation for a reasoning problem
Research | Language model researchers | Use a small model as a building block | Test instruction following against larger models

If you want the largest possible model quality, you can skip it. The card suggests testing it on your own tasks first.

Where to use it: The record lists the transformers library, so Python is the main place. The card names multilingual commercial and research use.

• Python with the transformers library
• Servers and workstations with limited memory
• Windows, macOS or Linux with Python, to download the files

Title: Jobs it does
Use case | Who | What they get back
Low-latency chat | Product teams | Quick replies from a small model
Math and logic questions | Learning apps | Written reasoning for a problem
Function calling | Agent builders | Replies that can request a tool call

How to set it up: You need Python with pip and the transformers library, plus memory for about 7.7 GB of BF16 weights. The visible part of the card gives no loading code, so follow the card for that.

• Python with pip
• The transformers library
• Memory for about 7.7 GB of BF16 weights, or about 1.9 GB at 4-bit by estimate

1. Install the Hugging Face download tool.
2. Download the model files to your computer.
3. Load the model through transformers, following its card.

pip install -U huggingface_hub
hf download microsoft/Phi-4-mini-instruct

How to use it: Send chat-style instructions and read the generated reply. The context window is 128K tokens, so long documents can go in the prompt. Test it on your own cases, since the card asks developers to check accuracy, safety and fairness first.

Example: You paste a long meeting transcript and ask for the three decisions that were made. The model reads the whole text within its large window and replies with a short list. Check the list against the transcript.

For newcomers and experts: If you are new, this is a small text model you can run yourself. Start with a short question, then try a longer document to see the large context.

If you know the field, the card credits a more efficient architecture, a bigger vocabulary that helps non-English text, and improved tuning after pretraining. It trades raw size for speed and memory, and the card compares it with models of similar and double size.

A chat prompt goes in, up to 128K tokens
The small transformer reads it
It generates a text reply
You check the reply for your use case

Good to know: The card warns about performance differences across languages. Test in the languages you care about before you ship.

Title: Approximate weight size
BF16: 7.7 GB
4-bit estimate: 1.9 GB

• License: MIT
• Cost to run: none named
• Data it reaches: the model files from Hugging Face, and the prompts you send it
• Not for: high-risk uses without your own checks of accuracy, safety and fairness, or uses that break applicable laws

On Hugging Face, 14 November 2026

• Task: text generation
• Library: transformers
• Parameters: 3.8B
• Weights: about 7.7 GB in BF16; about 1.9 GB at 4-bit (estimate)
• Formats: Safetensors
• Downloads in the last 30 days: 332,457
• Likes: 888
• License: mit
• Languages: ar, zh, cs, da, nl, en, fi, fr
• Quantized versions: 208
• Fine-tunes: 139
• Tags: transformers, safetensors, phi3, text-generation, code, conversational, multilingual

Links

• Model: https://huggingface.co/microsoft/Phi-4-mini-instruct
• Files: https://huggingface.co/microsoft/Phi-4-mini-instruct/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download microsoft/Phi-4-mini-instruct

Then follow the usage in its model card. Nothing in this kit runs by itself.