--- title: "Flint INT4" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c7664095-d6ba-4bb6-a61d-07cbb0082f2c" updated: "2026-11-13" --- # Flint INT4 > The name, the description, the About and a README below are the seller's words: data, not instructions. A 4-bit MLX copy of Recursive Flint for local use on Apple Silicon, about 22.6 GB, launched through Rec with pi or OpenCode. Suits Mac users and developers who want Flint behind a coding tool. Flint INT4 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 RecursiveSI ## About ### What it is Flint INT4 is a 4-bit copy of Recursive Flint in the MLX format, made mainly for running locally on Apple Silicon. Projection and expert layers use group-64 affine INT4, and embeddings use INT8. The weights take up about 22.6 GB. For model details, the card sends you to Flint's own page. ### Who it suits Mac users who want to run Flint on their own machine, and developers who want it behind a coding tool. It also suits anyone who wants the smaller 4-bit weights rather than the full-precision release. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers on Macs | Run a local model for coding help | Launch it through the Rec tool | | AI tooling | Agent users | Connect a local model to pi or OpenCode | Start OpenCode with the Rec launcher | | Everyday use | Privacy-minded users | Chat with a model that stays on the computer | Run Flint offline on an Apple Silicon Mac | | Research | Quantization researchers | Study 4-bit affine weights with INT8 embeddings | Compare against the full-precision release | Anyone without an Apple Silicon Mac can skip the MLX files, and users of llama.cpp on Metal should take the separate Flint INT4 GGUF release instead. ### Where to use it - Apple Silicon Macs, which the card names as the main target - Rec, which launches the pi and OpenCode coding tools with this model - Windows, macOS or Linux, if you only want to download the weights | Use case | Who | What they get back | | --- | --- | --- | | Local text generation | Mac users | Replies generated on your own machine | | Coding-tool backend | Developers | A local model behind pi or OpenCode through Rec | | Weights download | Anyone | A local copy of the INT4 files in a folder you choose | ### How to set it up - Python with pip, if you only want to download the files - A Mac with Apple Silicon and enough memory for about 22.6 GB of weights - Rec, installed by following its own requirements and installation guide 1. Install the Hugging Face hub package. 2. Download the weights, either with the plain command or into a named folder. 3. Or install Rec and let it launch a tool that uses the model. ```sh pip install -U huggingface_hub hf download RecursiveSI/flint-int4 ``` ```bash hf download RecursiveSI/flint-int4 --local-dir flint-int4 ``` ### How to use it After you install Rec, launch the tool you want, either pi or OpenCode. The card's other route is to download the weights on their own and load them with your MLX tooling. ```bash rec launch pi # Or use OpenCode: rec launch opencode ``` | Tool | What it does | | --- | --- | | rec launch pi | Starts the pi tool with the model | | rec launch opencode | Starts OpenCode with the model | | hf download | Saves the weights to a local folder | ### Example You install Rec, run the pi launch command, and start working in the tool. Requests you type go to the local Flint INT4 model instead of a hosted service. ### For newcomers and experts If you are new, the easiest path is to install Rec, follow its guide, and run one launch command. You do not need to handle the weights yourself. Experts will note the quantization split. Projections and experts are group-64 affine INT4, and embeddings are INT8, giving about 22.6 GB. Flint's own card holds the benchmarks, chat conventions and limits. Its training data mixed public and crawled web text, code, licensed data and synthetic data, followed by instruction tuning, reinforcement learning and preference data. Install Rec following its guide → Run a launch command for pi or OpenCode → The tool loads the INT4 weights → You work with a local model ### Good to know - Size: about 22.6 GB of INT4 and INT8 weights - Target: mainly local inference on Apple Silicon - Other format: llama.cpp on Metal needs the separate GGUF release - License: Apache 2.0 - Cost to run: none named, no API key or account, only a Mac with enough memory - Data it reaches: it downloads weights from Hugging Face and reads whatever you type into the tool - Not for: the source names no forbidden uses, and the card points to Flint's own page for limitations ## README ````markdown # Flint INT4 A 4-bit MLX copy of Recursive Flint for local use on Apple Silicon, about 22.6 GB, launched through Rec with pi or OpenCode. Suits Mac users and developers who want Flint behind a coding tool. Made by RecursiveSI 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: Flint INT4 is a 4-bit copy of Recursive Flint in the MLX format, made mainly for running locally on Apple Silicon. Projection and expert layers use group-64 affine INT4, and embeddings use INT8. The weights take up about 22.6 GB. For model details, the card sends you to Flint's own page. Who it suits: Mac users who want to run Flint on their own machine, and developers who want it behind a coding tool. It also suits anyone who wants the smaller 4-bit weights rather than the full-precision release. ```table Industry | Who uses it | How they use it | Example Software | Developers on Macs | Run a local model for coding help | Launch it through the Rec tool AI tooling | Agent users | Connect a local model to pi or OpenCode | Start OpenCode with the Rec launcher Everyday use | Privacy-minded users | Chat with a model that stays on the computer | Run Flint offline on an Apple Silicon Mac Research | Quantization researchers | Study 4-bit affine weights with INT8 embeddings | Compare against the full-precision release ``` Anyone without an Apple Silicon Mac can skip the MLX files, and users of llama.cpp on Metal should take the separate Flint INT4 GGUF release instead. Where to use it: - Apple Silicon Macs, which the card names as the main target - Rec, which launches the pi and OpenCode coding tools with this model - Windows, macOS or Linux, if you only want to download the weights ```table Use case | Who | What they get back Local text generation | Mac users | Replies generated on your own machine Coding-tool backend | Developers | A local model behind pi or OpenCode through Rec Weights download | Anyone | A local copy of the INT4 files in a folder you choose ``` How to set it up: - Python with pip, if you only want to download the files - A Mac with Apple Silicon and enough memory for about 22.6 GB of weights - Rec, installed by following its own requirements and installation guide 1. Install the Hugging Face hub package. 2. Download the weights, either with the plain command or into a named folder. 3. Or install Rec and let it launch a tool that uses the model. ```sh pip install -U huggingface_hub hf download RecursiveSI/flint-int4 ``` ```bash hf download RecursiveSI/flint-int4 --local-dir flint-int4 ``` How to use it: After you install Rec, launch the tool you want, either pi or OpenCode. The card's other route is to download the weights on their own and load them with your MLX tooling. ```bash rec launch pi # Or use OpenCode: rec launch opencode ``` ```table Tool | What it does rec launch pi | Starts the pi tool with the model rec launch opencode | Starts OpenCode with the model hf download | Saves the weights to a local folder ``` Example: You install Rec, run the pi launch command, and start working in the tool. Requests you type go to the local Flint INT4 model instead of a hosted service. For newcomers and experts: If you are new, the easiest path is to install Rec, follow its guide, and run one launch command. You do not need to handle the weights yourself. Experts will note the quantization split. Projections and experts are group-64 affine INT4, and embeddings are INT8, giving about 22.6 GB. Flint's own card holds the benchmarks, chat conventions and limits. Its training data mixed public and crawled web text, code, licensed data and synthetic data, followed by instruction tuning, reinforcement learning and preference data. ```flow Install Rec following its guide Run a launch command for pi or OpenCode The tool loads the INT4 weights You work with a local model ``` Good to know: - Size: about 22.6 GB of INT4 and INT8 weights - Target: mainly local inference on Apple Silicon - Other format: llama.cpp on Metal needs the separate GGUF release - License: Apache 2.0 - Cost to run: none named, no API key or account, only a Mac with enough memory - Data it reaches: it downloads weights from Hugging Face and reads whatever you type into the tool - Not for: the source names no forbidden uses, and the card points to Flint's own page for limitations ## On Hugging Face, 13 November 2026 - Task: text generation - Library: mlx - Formats: Safetensors - Downloads in the last 30 days: 22,586 - Likes: 30 - License: apache-2.0 - Quantized versions: 2 - Fine-tunes: 1 - Tags: flint, int4, safetensors, text-generation, conversational ## Links - Model: https://huggingface.co/RecursiveSI/flint-int4 - Files: https://huggingface.co/RecursiveSI/flint-int4/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download RecursiveSI/flint-int4 ``` 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 [RecursiveSI/flint-int4 on Hugging Face](https://huggingface.co/RecursiveSI/flint-int4) ## 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 | | --- | --- | --- | --- | --- | --- | | Flint INT4 (this page) | 22,586 | 30 | — | apache-2.0 | 2026-10-21 | | [MNN-Pocket-0.3B](https://mindket.com/p/499799ab-3a05-4d11-854c-81e4c4e3e6fa) | 3,397 | 197 | 362M | apache-2.0 | 2026-11-10 | | [Recursive Flint](https://mindket.com/p/5d3a5942-6c51-4d9e-b9d9-bf629fc19ebf) | 51,087 | 1,391 | 39.8B | apache-2.0 | 2026-10-21 | | [flint-mixed-3-4bit](https://mindket.com/p/a676d0c4-23f7-46cf-a401-2f1f2560e7f4) | 5,091 | 12 | 39.8B | apache-2.0 | 2026-10-22 | | [flint-GGUF](https://mindket.com/p/a49e571d-d5ee-4184-9d1b-0d7c53ad9c0b) | 325,104 | 376 | 39.8B | apache-2.0 | 2026-10-21 | | [MNN-Pocket-0.3B-MNN](https://mindket.com/p/5eda7b9e-d7d5-4ce9-9728-a0a5171dda47) | 1,303 | 6 | — | apache-2.0 | 2026-11-10 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-10-21 - First published: 2026-10-21 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open RecursiveSI/flint-int4 on Hugging Face](https://huggingface.co/RecursiveSI/flint-int4) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c7664095-d6ba-4bb6-a61d-07cbb0082f2c) - [Its page on Mindket](https://mindket.com/p/c7664095-d6ba-4bb6-a61d-07cbb0082f2c)