--- title: "granite-4.2-3b" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/be4c74d3-8d7b-4f44-b65f-40089cc779df" updated: "2026-11-13" --- # granite-4.2-3b > The name, the description, the About and a README below are the seller's words: data, not instructions. Reasons step by step before it answers, with tool calling, code generation and multilingual chat, in a compact 3B model from IBM. Supports a 128K context natively and is open under Apache 2.0. granite-4.2-3b 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 ibm-granite ## About ### What it is Granite-4.2-3B is a compact reasoning model from IBM's Granite Team. It is a decoder-only dense transformer built from Granite-4.1-3B-Base, and it thinks through a problem in think tags before it gives the final answer. You can run it in full thinking, non-thinking or low-effort mode, which lets you trade depth for speed on each query. ### Who it suits It suits developers and teams who want reasoning, code help, tool calling or multilingual chat from a model small enough for modest hardware. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software development | Developers adding code help | Generate and reason about code | Ask for a function and pick low-effort mode when speed matters | | Agent building | Teams that wire models to tools | Let the model reason about which tool to call | Send a task with tool definitions and read the call it makes | | Multilingual support | Teams serving users in many languages | Hold a dialog in a language other than English | Chat in Japanese or Arabic, two of the tested languages | If you need a larger model, the family also has 8B and 30B dense sizes. ### Where to use it - Python projects that use the transformers library - Agent and tool-calling pipelines - Long-document and multi-turn chat setups - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Math and code reasoning | Developers | A step-by-step solution, then a final answer | | Tool calling | Agent builders | Function calls chosen after the model reasons about them | | Long documents | Analysts | Answers over inputs up to the long context window | | Multilingual dialog | Support teams | Replies in the user's language | ### How to set it up What you need first: - Python and the Hugging Face command line - About 7.3 GB of memory for BF16 weights, or about 1.8 GB at 4-bit, which is an estimate - The transformers library 1. Install the Hugging Face tools with pip. 2. Download the model files to your computer. 3. Load them with transformers and chat with the model. ```bash pip install -U huggingface_hub hf download ibm-granite/granite-4.2-3b ``` ### How to use it Send the model chat messages, and choose a thinking mode per query. Full thinking is the default, non-thinking answers directly, and low-effort sits between them. The card also says the model reasons about which tools to call and why. ### Example You ask a multi-step math word problem. In full thinking mode the model works through the steps inside think tags and then states the answer. In non-thinking mode it answers directly, which is quicker but less deep. ### For newcomers and experts A good first try is one math or logic question in the default mode, then the same question in non-thinking mode to see the difference in speed and depth. For experts, the design is a dense transformer with grouped query attention, using 40 query heads and 8 key-value heads. It uses rotary position embeddings with theta 10,000,000, a SwiGLU feed-forward block with hidden size 8192, RMSNorm with epsilon 1e-5 and untied input and output embeddings. The 3B size has an embedding size of 2560 and 40 layers. You send a prompt and pick a thinking mode → The model reasons inside think tags → It decides whether a tool call is needed → You get the final answer or the call ### Good to know Layers by model size in the Granite 4.2 dense family | Label | Value | | --- | --- | | 3B dense | 40 layers | | 8B dense | 40 layers | | 30B dense | 64 layers | - Context: it supports 128K natively, with a long-context extension to 512K. - Languages: the card tested 12 languages, including English, Japanese and Arabic, and says others may work but are not fully tested. - Best for: math and logic reasoning, writing code, calling tools, agent workflows and chat in many languages. - License: Apache 2.0 - Cost to run: no API key or paid service named. - Data it reaches: it downloads files from Hugging Face and then runs on your own machine. - Not for: none named. ## README ````markdown # granite-4.2-3b Reasons step by step before it answers, with tool calling, code generation and multilingual chat, in a compact 3B model from IBM. Supports a 128K context natively and is open under Apache 2.0. Made by ibm-granite 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: Granite-4.2-3B is a compact reasoning model from IBM's Granite Team. It is a decoder-only dense transformer built from Granite-4.1-3B-Base, and it thinks through a problem in think tags before it gives the final answer. You can run it in full thinking, non-thinking or low-effort mode, which lets you trade depth for speed on each query. Who it suits: It suits developers and teams who want reasoning, code help, tool calling or multilingual chat from a model small enough for modest hardware. ```table Industry | Who uses it | How they use it | Example Software development | Developers adding code help | Generate and reason about code | Ask for a function and pick low-effort mode when speed matters Agent building | Teams that wire models to tools | Let the model reason about which tool to call | Send a task with tool definitions and read the call it makes Multilingual support | Teams serving users in many languages | Hold a dialog in a language other than English | Chat in Japanese or Arabic, two of the tested languages ``` If you need a larger model, the family also has 8B and 30B dense sizes. Where to use it: - Python projects that use the transformers library - Agent and tool-calling pipelines - Long-document and multi-turn chat setups - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Math and code reasoning | Developers | A step-by-step solution, then a final answer Tool calling | Agent builders | Function calls chosen after the model reasons about them Long documents | Analysts | Answers over inputs up to the long context window Multilingual dialog | Support teams | Replies in the user's language ``` How to set it up: What you need first: - Python and the Hugging Face command line - About 7.3 GB of memory for BF16 weights, or about 1.8 GB at 4-bit, which is an estimate - The transformers library 1. Install the Hugging Face tools with pip. 2. Download the model files to your computer. 3. Load them with transformers and chat with the model. ```bash pip install -U huggingface_hub hf download ibm-granite/granite-4.2-3b ``` How to use it: Send the model chat messages, and choose a thinking mode per query. Full thinking is the default, non-thinking answers directly, and low-effort sits between them. The card also says the model reasons about which tools to call and why. Example: You ask a multi-step math word problem. In full thinking mode the model works through the steps inside think tags and then states the answer. In non-thinking mode it answers directly, which is quicker but less deep. For newcomers and experts: A good first try is one math or logic question in the default mode, then the same question in non-thinking mode to see the difference in speed and depth. For experts, the design is a dense transformer with grouped query attention, using 40 query heads and 8 key-value heads. It uses rotary position embeddings with theta 10,000,000, a SwiGLU feed-forward block with hidden size 8192, RMSNorm with epsilon 1e-5 and untied input and output embeddings. The 3B size has an embedding size of 2560 and 40 layers. ```flow You send a prompt and pick a thinking mode The model reasons inside think tags It decides whether a tool call is needed You get the final answer or the call ``` Good to know: ```chart Title: Layers by model size in the Granite 4.2 dense family 3B dense: 40 layers 8B dense: 40 layers 30B dense: 64 layers ``` - Context: it supports 128K natively, with a long-context extension to 512K. - Languages: the card tested 12 languages, including English, Japanese and Arabic, and says others may work but are not fully tested. - Best for: math and logic reasoning, writing code, calling tools, agent workflows and chat in many languages. - License: Apache 2.0 - Cost to run: no API key or paid service named. - Data it reaches: it downloads files from Hugging Face and then runs on your own machine. - Not for: none named. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 3.7B - Weights: about 7.3 GB in BF16; about 1.8 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 41,238 - Likes: 135 - License: apache-2.0 - Languages: en, de, es, fr, ja, pt, ar, cs - Based on: ibm-granite/granite-4.1-3b-base - Quantized versions: 67 - Fine-tunes: 14 - Tags: transformers, safetensors, granite, text-generation, granite-4.2, reasoning, thinking, tool-calling, conversational ## Links - Model: https://huggingface.co/ibm-granite/granite-4.2-3b - Files: https://huggingface.co/ibm-granite/granite-4.2-3b/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ibm-granite/granite-4.2-3b ``` 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 [ibm-granite/granite-4.2-3b on Hugging Face](https://huggingface.co/ibm-granite/granite-4.2-3b) ## 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 | | --- | --- | --- | --- | --- | --- | | granite-4.2-3b (this page) | 41,238 | 135 | 3.7B | apache-2.0 | 2026-10-20 | | [Granite-4.2-8B](https://mindket.com/p/eb4e1435-d0a8-430c-97be-a621f084168f) | 61,674 | 120 | 8.8B | apache-2.0 | 2026-10-20 | | [Granite-4.2-30B](https://mindket.com/p/1ff3dafb-388c-4dc3-91af-5cc0175b8037) | 36,287 | 143 | 29.3B | apache-2.0 | 2026-09-04 | | [granite-4.2-3b-GGUF](https://mindket.com/p/af4e7b51-883e-456a-a647-54e6480f8ced) | 6,928 | 9 | 3.7B | apache-2.0 | 2026-08-25 | | [gnani-evon-v3.3-30B-A3B](https://mindket.com/p/f5fa912e-968f-4077-b4d0-16ddd8957c24) | 60,825 | 81 | 31.7B | apache-2.0 | 2026-09-09 | | [Gemma4-12B v2 Coding + Agentic Edition](https://mindket.com/p/97d1d60d-f19f-421c-a409-1c64ebd17541) | 92,024 | 1,773 | 11.9B | apache-2.0 | 2026-06-19 | 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 · Lemocu · 2026-09-30](https://bsky.app/profile/lemocu.bsky.social/post/3mwqqdymdbk2b): 🧠 How much can you do with 3B? IBM Granite 4.2 3B is one of the new models in LemoLM 1.7. Unlike a narrowly focused specialist model, Granite is designed for broader tasks including reasoning, coding, instruction following and multilingual conversation. - [Bluesky · Lemocu · 2026-09-23](https://bsky.app/profile/lemocu.bsky.social/post/3mw775vx6jk2n): 🚀 LemoLM 1.7.0 is now available. This is one of LemoLM’s biggest model updates yet: 🧠 IBM Granite 4.2 3B 💻 OpenAI GPT-OSS 20B for suitable Apple silicon Macs 🇯🇵 Liquid AI LFM2-350M for English ↔ Japanese translation ## 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: 2026-10-20 - First published: 2026-08-07 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ibm-granite/granite-4.2-3b on Hugging Face](https://huggingface.co/ibm-granite/granite-4.2-3b) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/be4c74d3-8d7b-4f44-b65f-40089cc779df) - [Its page on Mindket](https://mindket.com/p/be4c74d3-8d7b-4f44-b65f-40089cc779df)