--- title: "flint-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a49e571d-d5ee-4184-9d1b-0d7c53ad9c0b" updated: "2026-11-12" --- # flint-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs Flint, a 39.8B mixture-of-experts model for coding agents that uses about 2.5B parameters per token, from Unsloth GGUF files. For developers who run coding agents locally. flint-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by unsloth ## About ### What it is flint-GGUF holds Unsloth GGUF versions of Flint, a language model from Recursive built for coding agents. It has 39.8 billion parameters in total, and about 2.5 billion are active for each token. Flint is meant to explore codebases, fix bugs, add features and write tests. It reads and writes text, reasons, and supports multi-turn tool use. ### Who it suits Developers who want to run a coding agent model on their own machine, and teams that compare local models on software engineering tasks. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers using coding agents | Run a local model behind an agent | Have it fix a bug in a repository | | Engineering teams | Teams that maintain large codebases | Let an agent write tests | Generate tests for a new feature | | Research | People comparing coding models | Score it on software engineering tasks | Compare it with other local models | Skip it if you need image or audio input, since Flint takes text in and gives text out. ### Where to use it - A local runner that opens GGUF files - Unsloth Desktop, which the card shows with thinking controls - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Bug fixing | Developers | Code changes proposed after the model investigates a codebase | | Feature work | Agent builders | Implementations produced over several tool-using turns | | Test writing | Maintainers | New tests for existing code | ### How to set it up - Python and pip to run the download commands - Enough memory for the files, since the 4-bit weights are about 19.9 GB by Hugging Face estimate - A GGUF-capable runner, or the Unsloth guide for running Flint 1. Install the Hugging Face tools. 2. Download the GGUF files from this repository. 3. Open them in your runner, following the Unsloth guide for Flint. ```bash pip install -U huggingface_hub hf download unsloth/flint-GGUF ``` ### How to use it Load the files in your runner and connect an agent or chat to it. The authors ran their evaluations with a temperature of 1.0, a top-p of 0.95 and a top-k of 20. | Property | Flint | | --- | --- | | Total parameters | 39.8B | | Active per token | About 2.5B | | Layers | 49 in all, the first 2 dense and the other 47 mixture-of-experts | | Attention | 35 layers look at a local window and 14 layers attend to the full context | | Experts | 256 routed, 6 chosen per token, plus one shared expert that is always on | | Context | 262,144 tokens in the documented setup | ### Example You point a coding agent at a repository with a failing test and ask it to find and fix the bug. The model investigates the code over several turns, calls tools and proposes a fix. ### For newcomers and experts If you are new to coding agents, start by loading the files and asking one plain coding question. Move to an agent setup once the basic chat works. If you know the field, it is a decoder-only mixture-of-experts transformer with sliding-window attention on most layers. Each MoE layer routes a token to 6 of 256 experts and also runs one shared expert. Download the GGUF files → Open them in a local runner → Connect a coding agent → Model investigates, edits and tests ### Good to know - The authors measured the scores and ran Terminal-Bench with Terminus 2 and SWE-bench with mini-swe-agent - Their Terminal-Bench score counts solutions found through web retrieval as failures - The original repository holds the BF16 checkpoint, and quantized variants are prepared separately - Hugging Face lists 1 quantized and 1 fine-tuned version made by other people Release evaluation scores by the authors | Label | Value | | --- | --- | | Terminal-Bench 2.1 | 54.9 percent | | SWE-bench Pro v2 | 89.7 percent | | SWE-bench Verified | 74.2 percent | - License: Apache 2.0 - Cost to run: none named - Data it reaches: the files you download and the code and prompts you give it - Not for: input other than text ## README ````markdown # flint-GGUF Runs Flint, a 39.8B mixture-of-experts model for coding agents that uses about 2.5B parameters per token, from Unsloth GGUF files. For developers who run coding agents locally. Made by unsloth 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-GGUF holds Unsloth GGUF versions of Flint, a language model from Recursive built for coding agents. It has 39.8 billion parameters in total, and about 2.5 billion are active for each token. Flint is meant to explore codebases, fix bugs, add features and write tests. It reads and writes text, reasons, and supports multi-turn tool use. Who it suits: Developers who want to run a coding agent model on their own machine, and teams that compare local models on software engineering tasks. ```table Industry | Who uses it | How they use it | Example Software | Developers using coding agents | Run a local model behind an agent | Have it fix a bug in a repository Engineering teams | Teams that maintain large codebases | Let an agent write tests | Generate tests for a new feature Research | People comparing coding models | Score it on software engineering tasks | Compare it with other local models ``` Skip it if you need image or audio input, since Flint takes text in and gives text out. Where to use it: - A local runner that opens GGUF files - Unsloth Desktop, which the card shows with thinking controls - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Bug fixing | Developers | Code changes proposed after the model investigates a codebase Feature work | Agent builders | Implementations produced over several tool-using turns Test writing | Maintainers | New tests for existing code ``` How to set it up: - Python and pip to run the download commands - Enough memory for the files, since the 4-bit weights are about 19.9 GB by Hugging Face estimate - A GGUF-capable runner, or the Unsloth guide for running Flint 1. Install the Hugging Face tools. 2. Download the GGUF files from this repository. 3. Open them in your runner, following the Unsloth guide for Flint. ```bash pip install -U huggingface_hub hf download unsloth/flint-GGUF ``` How to use it: Load the files in your runner and connect an agent or chat to it. The authors ran their evaluations with a temperature of 1.0, a top-p of 0.95 and a top-k of 20. ```table Property | Flint Total parameters | 39.8B Active per token | About 2.5B Layers | 49 in all, the first 2 dense and the other 47 mixture-of-experts Attention | 35 layers look at a local window and 14 layers attend to the full context Experts | 256 routed, 6 chosen per token, plus one shared expert that is always on Context | 262,144 tokens in the documented setup ``` Example: You point a coding agent at a repository with a failing test and ask it to find and fix the bug. The model investigates the code over several turns, calls tools and proposes a fix. For newcomers and experts: If you are new to coding agents, start by loading the files and asking one plain coding question. Move to an agent setup once the basic chat works. If you know the field, it is a decoder-only mixture-of-experts transformer with sliding-window attention on most layers. Each MoE layer routes a token to 6 of 256 experts and also runs one shared expert. ```flow Download the GGUF files Open them in a local runner Connect a coding agent Model investigates, edits and tests ``` Good to know: - The authors measured the scores and ran Terminal-Bench with Terminus 2 and SWE-bench with mini-swe-agent - Their Terminal-Bench score counts solutions found through web retrieval as failures - The original repository holds the BF16 checkpoint, and quantized variants are prepared separately - Hugging Face lists 1 quantized and 1 fine-tuned version made by other people ```chart Title: Release evaluation scores by the authors Terminal-Bench 2.1: 54.9 percent SWE-bench Pro v2: 89.7 percent SWE-bench Verified: 74.2 percent ``` - License: Apache 2.0 - Cost to run: none named - Data it reaches: the files you download and the code and prompts you give it - Not for: input other than text ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 39.8B - Weights: about 19.9 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 325,104 - Likes: 376 - License: apache-2.0 - Based on: RecursiveSI/flint - Quantized versions: 1 - Fine-tunes: 1 - Tags: gguf, flint, unsloth, text-generation, imatrix, conversational ## Links - Model: https://huggingface.co/unsloth/flint-GGUF - Files: https://huggingface.co/unsloth/flint-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download unsloth/flint-GGUF ``` 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 [unsloth/flint-GGUF on Hugging Face](https://huggingface.co/unsloth/flint-GGUF) ## 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-GGUF (this page) | 325,104 | 376 | 39.8B | apache-2.0 | 2026-10-21 | | [Qwen3-30B-A3B-GGUF by unsloth](https://mindket.com/p/7f9e4803-e508-4446-89f0-cc5cc0837876) | 53,664 | 294 | 30.5B | apache-2.0 | 2025-06-08 | | [Qwen3.6-35B-A3B-MTP-GGUF](https://mindket.com/p/90db7fe0-7044-4238-a508-5d2ecb760999) | 2,315,167 | 1,051 | 35.5B | apache-2.0 | 2026-05-20 | | [Qwen3.6-35B-A3B-GGUF](https://mindket.com/p/9ac02ca9-6ba3-4ea0-b3f6-dcaea6e7789f) | 1,649,871 | 1,789 | 34.7B | apache-2.0 | 2026-04-20 | | [DeepSeek-V4.1-Flash-1017-GGUF](https://mindket.com/p/1c185af8-98d2-4feb-a1a3-d5821c72863f) | 1,295,742 | 385 | 748B | mit | 2026-11-02 | | [gemma-4-31B-it-GGUF](https://mindket.com/p/f257bf61-ef21-40bb-9e67-e4d77bf5e0e0) | 428,266 | 677 | 30.7B | apache-2.0 | 2026-07-17 | 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 · Unsloth AI · 2026-10-21](https://bsky.app/profile/unsloth.ai/post/3mygbzip5cs2e): Run Flint locally on 24GB RAM via Unsloth Dynamic GGUFs\! Flint is the strongest MoE model under 50B parameters. Recursive’s Apache-2.0 model has 39.8B parameters (2.5B active). Run at 125 t/s on Mac, 150 on RTX Spark GGUF: huggingface.co/unsloth/flin... Guide: unsloth.ai/docs/models/... - [Bluesky · UndergroundAI · 2026-10-22](https://bsky.app/profile/undergroundai66.bsky.social/post/3myipbv6vme2d): ⚡ Das Trend-Modell auf HuggingFace im Detail. 🛠️ Das von unsloth entwickelte Sprachmodell flint-GGUF begeistert aktuell die Community auf… 🚀 Entdecke diesen und weitere KI-Geheimtipps auf Underground AI. 👉 https://underground-ai.bolt.app/ \#OpenSource \#KuenstlicheIntelligenz - [Bluesky · Hackobar · 2026-10-25](https://bsky.app/profile/hackobar.bsky.social/post/3mypbbc6wyb2q): 9. Unsloth releases flint-GGUF quantized model weights LINK 10. Ollama Integrates Phonon Speech Model for High-Speed Text-to-Speech LINK ## 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: 2026-10-20 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open unsloth/flint-GGUF on Hugging Face](https://huggingface.co/unsloth/flint-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a49e571d-d5ee-4184-9d1b-0d7c53ad9c0b) - [Its page on Mindket](https://mindket.com/p/a49e571d-d5ee-4184-9d1b-0d7c53ad9c0b)