--- title: "phi-2" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/176aceda-06bf-4fd5-bb76-cc752eeba37c" updated: "2026-11-13" --- # phi-2 > The name, the description, the About and a README below are the seller's words: data, not instructions. A small text generation model that answers questions, continues chat dialogue and completes code. Made for researchers and developers who want a compact open model for safety studies and experiments. phi-2 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-2 is a small Transformer language model from Microsoft with about 2.7 billion parameters. It writes text from a prompt, in question and answer, chat or code layouts. It was trained on the same data as Phi-1.5 plus synthetic NLP texts and filtered websites. It has not been tuned with reinforcement learning from human feedback. ### Who it suits It suits researchers and developers who want a small, unrestricted model to study toxicity, bias and controllability, or to test ideas on modest hardware. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI safety research | Research teams | Study toxicity, societal bias and controllability on a small open model | Probing how the model reacts to risky prompts | | Education | Teachers and students | Run a small model locally to see how prompting works | Trying question, chat and code prompts side by side | | Software | Developers | Prototype code completion or question answering | Completing a Python function from its comment | Skip it for production work, since the card says it was not tested for any production-level application. ### Where to use it - Python scripts using the transformers library, version 4.37.0 or higher - Windows, macOS and Linux - A GPU, since the card's sample code sets cuda as the default device | Use case | Who | What they get back | | --- | --- | --- | | Question answering | Researchers | A written answer after the question | | Chat continuation | Developers | The next reply in a dialogue | | Code completion | Developers | A function body after the comment | ### How to set it up - Python with a recent transformers library and torch - About 5.6 GB of disk space for the F16 weights, or about 1.4 GB at 4-bit as an estimate - A GPU for the card's sample code 1. Install the Hugging Face tools. 2. Download the model files, or let the sample code fetch them. 3. Run the sample code, or write your own prompt in one of the three formats. ```sh pip install -U huggingface_hub hf download microsoft/phi-2 ``` ### How to use it You write a prompt in the question, chat or code layout, and the model continues the text. For shorter answers, use the Instruct and Output layout. The card warns that an FP16 attention overflow can occur, and says older transformers versions need trust\_remote\_code set to True. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer torch.set_default_device("cuda") model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True) outputs = model.generate(**inputs, max_length=200) text = tokenizer.batch_decode(outputs)[0] print(text) ``` | Format | What it does | | --- | --- | | Question | A standalone question, with the answer written after it | | Instruct and Output | Asks for a more concise answer after the Output label | | Chat | Names like Alice and Bob, and the model writes the next reply | | Code | A function signature and comment, and the model writes the body | ### Example You ask the model to compare mathematics to a lighthouse, using the Instruct line and an empty Output line. It continues after the Output label with a short written comparison. Treat the result as a starting point and check it yourself. ### For newcomers and experts Phi-2 is a small model that continues whatever text you start. A good first try is the card's lighthouse prompt, and then a short chat between two named people to see how it carries on. If you know the field, this is a base-style Transformer without reinforcement learning from human feedback. That makes it a plain research subject for safety work. Expect the FP16 attention overflow the card describes, and expect its limits on accuracy for both code and facts. Write a prompt in one of three formats → Tokenizer turns it into input → The model generates new tokens → Decode the tokens into text → Check the result yourself ### Good to know - Accuracy: the model can produce wrong code and wrong statements, so treat output as a starting point. - Production: the card puts direct production use without evaluation out of scope. - Version: the card says transformers 4.37.0 or higher. - License: MIT. - Cost to run: none named. - Data it reaches: it downloads model files from Hugging Face and works on the text you give it. - Not for: production use without evaluation, or any reliance on its answers as definitive. ## README ````markdown # phi-2 A small text generation model that answers questions, continues chat dialogue and completes code. Made for researchers and developers who want a compact open model for safety studies and experiments. 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-2 is a small Transformer language model from Microsoft with about 2.7 billion parameters. It writes text from a prompt, in question and answer, chat or code layouts. It was trained on the same data as Phi-1.5 plus synthetic NLP texts and filtered websites. It has not been tuned with reinforcement learning from human feedback. Who it suits: It suits researchers and developers who want a small, unrestricted model to study toxicity, bias and controllability, or to test ideas on modest hardware. ```table Industry | Who uses it | How they use it | Example AI safety research | Research teams | Study toxicity, societal bias and controllability on a small open model | Probing how the model reacts to risky prompts Education | Teachers and students | Run a small model locally to see how prompting works | Trying question, chat and code prompts side by side Software | Developers | Prototype code completion or question answering | Completing a Python function from its comment ``` Skip it for production work, since the card says it was not tested for any production-level application. Where to use it: - Python scripts using the transformers library, version 4.37.0 or higher - Windows, macOS and Linux - A GPU, since the card's sample code sets cuda as the default device ```table Use case | Who | What they get back Question answering | Researchers | A written answer after the question Chat continuation | Developers | The next reply in a dialogue Code completion | Developers | A function body after the comment ``` How to set it up: - Python with a recent transformers library and torch - About 5.6 GB of disk space for the F16 weights, or about 1.4 GB at 4-bit as an estimate - A GPU for the card's sample code 1. Install the Hugging Face tools. 2. Download the model files, or let the sample code fetch them. 3. Run the sample code, or write your own prompt in one of the three formats. ```sh pip install -U huggingface_hub hf download microsoft/phi-2 ``` How to use it: You write a prompt in the question, chat or code layout, and the model continues the text. For shorter answers, use the Instruct and Output layout. The card warns that an FP16 attention overflow can occur, and says older transformers versions need trust_remote_code set to True. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer torch.set_default_device("cuda") model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True) outputs = model.generate(**inputs, max_length=200) text = tokenizer.batch_decode(outputs)[0] print(text) ``` ```table Format | What it does Question | A standalone question, with the answer written after it Instruct and Output | Asks for a more concise answer after the Output label Chat | Names like Alice and Bob, and the model writes the next reply Code | A function signature and comment, and the model writes the body ``` Example: You ask the model to compare mathematics to a lighthouse, using the Instruct line and an empty Output line. It continues after the Output label with a short written comparison. Treat the result as a starting point and check it yourself. For newcomers and experts: Phi-2 is a small model that continues whatever text you start. A good first try is the card's lighthouse prompt, and then a short chat between two named people to see how it carries on. If you know the field, this is a base-style Transformer without reinforcement learning from human feedback. That makes it a plain research subject for safety work. Expect the FP16 attention overflow the card describes, and expect its limits on accuracy for both code and facts. ```flow Write a prompt in one of three formats Tokenizer turns it into input The model generates new tokens Decode the tokens into text Check the result yourself ``` Good to know: - Accuracy: the model can produce wrong code and wrong statements, so treat output as a starting point. - Production: the card puts direct production use without evaluation out of scope. - Version: the card says transformers 4.37.0 or higher. - License: MIT. - Cost to run: none named. - Data it reaches: it downloads model files from Hugging Face and works on the text you give it. - Not for: production use without evaluation, or any reliance on its answers as definitive. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 2.8B - Weights: about 5.6 GB in F16; about 1.4 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 182,126 - Likes: 3,536 - License: mit - Languages: en - Quantized versions: 64 - Fine-tunes: 416 - Tags: transformers, safetensors, text-generation, code ## Links - Model: https://huggingface.co/microsoft/phi-2 - Files: https://huggingface.co/microsoft/phi-2/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download microsoft/phi-2 ``` 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-2 on Hugging Face](https://huggingface.co/microsoft/phi-2) ## 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-2 (this page) | 182,126 | 3,536 | 2.8B | mit | 2025-12-08 | | [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 | | [Qwen2.5-Coder-7B](https://mindket.com/p/7119a7b6-0d21-4147-ab85-fddd89e38236) | 334,246 | 200 | 7.6B | apache-2.0 | 2024-11-18 | | [Phi-4-mini-instruct](https://mindket.com/p/64227072-1a9f-4a0f-9e9c-e5eb7f599b91) | 332,457 | 888 | 3.8B | mit | 2025-12-10 | 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. - [Hacker News · regularfry · 2024-01-06](https://news.ycombinator.com/item?id=38889539): Microsoft Phi-2 model changes licence to MIT - [Bluesky · genai-podcast.bsky.social · 2026-08-19](https://bsky.app/profile/genai-podcast.bsky.social/post/3mtfgez62u725): 🎧 EP075: Microsoft Phi Beats Giants With Synthetic Textbooks 🔗 https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/ 🟢 https://podcasters.spotify.com/pod/show/yun-wu/episodes/EP075-Microsoft-Phi-Beats-Giants-With-Synthetic-Textbooks-e3fnllo \#GenAI \#AI - [Hacker News · mjirv · 2023-12-12](https://news.ycombinator.com/item?id=38620339): Microsoft releases Phi-2, a small model that outperforms Llama 2, Mistral 7B - [Hacker News · tosh · 2023-11-15](https://news.ycombinator.com/item?id=38279733): Microsoft Releases Phi-2 - [Hacker News · thunderbong · 2023-12-15](https://news.ycombinator.com/item?id=38659350): Microsoft releases Phi-2, a powerful small language model AI - [Hacker News · webmaven · 2023-12-15](https://news.ycombinator.com/item?id=38653194): Microsoft releases Phi-2, a small language model - [Hacker News · taklaxbr · 2025-12-27](https://news.ycombinator.com/item?id=46403135): Show HN: I'm 15. I built an offline AI Terminal Agent that fixes errors: Hi HN I'm a 15 year old developer from Turkey. I previously shared an earlier version, but today I'm releasing ZAI Shell v7.0 (Agent Edition), which is a massive overhaul. I built this because I was frustrated with existing AI CLI tools that break easily or require complex setups. What makes v7.0… - [Hacker News · BramVanroy · 2024-05-02](https://news.ycombinator.com/item?id=40240154): Fietje: An open and efficient LLM for Dutch: Fietje is an adapated version of microsoft/phi-2, tailored to Dutch text generation by training on 28B tokens. It is small and efficient with a size of 2.7 billion parameters while performing almost on par with more powerful Dutch LLMs of twice its size like GEITje 7B Ultra. In some benchmarks it… - [Hacker News · r-k-jo · 2024-04-11](https://news.ycombinator.com/item?id=39998846): Llm.c – LLM training in simple, pure C/CUDA: You can also target WASM. Depending on the model size, it can be really good. Here are some examples of quantized models. Vision Model https://huggingface.co/spaces/radames/Candle-Moondream-2 Blip Image Captioning https://huggingface.co/spaces/radames/Candle-BLIP-Image-Capt... Microsoft Phi 2… ## 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: 2025-12-08 - First published: 2023-12-13 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open microsoft/phi-2 on Hugging Face](https://huggingface.co/microsoft/phi-2) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/176aceda-06bf-4fd5-bb76-cc752eeba37c) - [Its page on Mindket](https://mindket.com/p/176aceda-06bf-4fd5-bb76-cc752eeba37c)