--- title: "Phi-4-mini-instruct" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/64227072-1a9f-4a0f-9e9c-e5eb7f599b91" updated: "2026-11-13" --- # Phi-4-mini-instruct > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. Phi-4-mini-instruct is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by microsoft ## 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. 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 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. ```sh 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. Approximate weight size | Label | Value | | --- | --- | | 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh 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. ```flow 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. ```chart 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: ```sh 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. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [microsoft/Phi-4-mini-instruct on Hugging Face](https://huggingface.co/microsoft/Phi-4-mini-instruct) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Phi-4-mini-instruct (this page) | 332,457 | 888 | 3.8B | mit | 2025-12-10 | | [Phi-4](https://mindket.com/p/24d9cdd5-f11c-4640-9fa0-0804022f7709) | 236,972 | 2,343 | 14.7B | mit | 2026-07-14 | | [mSlideOn](https://mindket.com/p/e92bd39d-a0eb-440c-8a0d-8c22298b51ce) | 706 | 16 | 339M | apache-2.0 | 2026-10-20 | | [Qwen2.5-Coder-14B-Instruct](https://mindket.com/p/bf4cb9a2-fb0b-4f3c-965b-76d693eec384) | 1,860,327 | 214 | 14.8B | apache-2.0 | 2025-01-12 | | [Qwen2.5-Coder-7B-Instruct](https://mindket.com/p/26690bc4-bc2a-4e48-8691-e2d1eb40c60e) | 1,505,999 | 872 | 7.6B | apache-2.0 | 2025-01-12 | | [Qwen2.5-Coder-32B-Instruct](https://mindket.com/p/d7483fbe-56eb-45ae-b46b-3113572c1642) | 546,583 | 2,211 | 32.8B | apache-2.0 | 2025-01-12 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · Alex Galarce · 2026-02-18](https://bsky.app/profile/alexgalarce.bsky.social/post/3mf4daeaqb22i): Ok funnily enough, and you may be interested in this for yourself: the leading open-source small language model (ie fast to run locally on non-huge hardware) is Microsoft’s Phi: huggingface.co/microsoft/Ph... - [Bluesky · AWS News Feed on 🦋 · 2026-05-14](https://bsky.app/profile/awsrecentnews.bsky.social/post/3mltq4iym2a2s): 🆕 AWS added GLM-5.1-FP8 and Phi-4-mini-instruct to SageMaker JumpStart, featuring advanced coding and efficient AI models from Z.ai and Microsoft for software engineering, debugging, and reasoning, deployable with clicks for enterprise AI. \#AWS \#AmazonSagemaker \#Aiml \#AmazonSagemakerJumpstart - [Bluesky · AWS News(Unofficial) · 2026-05-14](https://bsky.app/profile/awsnews.bsky.social/post/5nmqs35zwv4ns): Two new models for agentic coding and efficient AI are now available in Amazon SageMaker JumpStart Today, AWS announced the availability of GLM-5.1-FP8 and Phi-4-mini-instruct in Amazon SageMaker JumpStart, expanding the portfolio of foundat... \#AWS \#AmazonSagemaker \#Aiml \#AmazonSagemakerJumpstart ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-12-10 - First published: 2025-02-19 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open microsoft/Phi-4-mini-instruct on Hugging Face](https://huggingface.co/microsoft/Phi-4-mini-instruct) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/64227072-1a9f-4a0f-9e9c-e5eb7f599b91) - [Its page on Mindket](https://mindket.com/p/64227072-1a9f-4a0f-9e9c-e5eb7f599b91)