--- title: "Muse Glimmer 30B GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/31b81c35-03e2-4c43-ad62-fe42f4b51f11" updated: "2026-11-13" --- # Muse Glimmer 30B GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs a 30B agent model locally that reads text and images, calls tools and retries failed calls, from Unsloth's GGUF files. For developers building local agents on consumer hardware. Muse Glimmer 30B GGUF 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 unsloth ## About ### What it is Muse Glimmer is a 30B dense language model with a dedicated perception encoder, built for agent work on consumer hardware. The card says it was distilled from Muse Spark and runs locally with no cloud or network access. This repository holds Unsloth's GGUF files for it. The model takes text and images in and writes text out. ### Who it suits It suits developers who build agents that run on their own machines, and people who want an assistant that can look at screenshots, charts and documents. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Agent developers | Run a local agent that calls tools and recovers from failures | Retry after a tool call returns an error | | Productivity | Power users | Ask about screenshots, charts and documents | Explain a chart from a screenshot | | Research | ML researchers | Study a distilled agent model and its quantizations | Compare Unsloth's quantized files | Anyone without about 14 GB of room for the 4-bit weights can skip it. ### Where to use it - Unsloth, where the card says it runs with toggles for thinking - Agent scaffolds such as OpenClaw and Hermes Agent, and other orchestration patterns - Your own machine, with no cloud service - Windows, macOS or Linux, where the files download with Python | Use case | Who | What they get back | | --- | --- | --- | | Finish a multi-step task | Agent developers | A completed workflow built from several tool calls | | Write and debug code | Developers | Code and fixes inside an agent scaffold | | Read a screenshot or chart | Everyday users | A text explanation of what the image shows | ### How to set it up - Python with pip, for the download - A runtime that can load GGUF files, such as Unsloth - Memory for about 13.9 GB of weights at 4-bit, as an estimate 1. Install the Hugging Face hub client. 2. Download the repository's files. 3. Load a quantization in Unsloth or another GGUF runtime. ```sh pip install -U huggingface_hub hf download unsloth/Muse-Glimmer-30B-GGUF ``` ### How to use it Run the model in Unsloth and switch thinking on or off with its toggles. The card points to Unsloth's own guide for quantization analysis and step-by-step instructions. | Tool | What it does | | --- | --- | | Unsloth | Runs the model locally, with toggles for thinking | | Perception encoder | Lets the model read text and images together, with up to 4,096 visual tokens per image | | Effort control | Picks a balance between answer quality and speed | | Tool calling | Calls functions with exact schemas across long workflows | ### Example You give it a screenshot of an error message and ask it to find the cause. If a tool call fails along the way, the card says the model works out why and tries again rather than stopping. ### For newcomers and experts Think of it as a local assistant that can look at pictures and use tools. Start with a plain text question in Unsloth, then try a screenshot. The language model is a dense causal transformer with 52 layers, a hidden width of 6656 and grouped-query attention with 32 query and 2 key-value heads. Attention repeats three local layers, then one global layer, with a 2048-token sliding window. RoPE applies to the local layers only. The perception encoder is a ViT-G/14 of about 1.8B parameters with 50 layers. Text and images go in → The perception encoder reads the images → The 30B model plans and calls tools → A failed call is diagnosed and retried → Text comes out ### Good to know - Context: 131,072 tokens or more - Languages: trained on data from more than 100 - Size: the card says about 29.6B parameters with the vision encoder, and the record lists 27.9B for these files - Tool calls: Unsloth's page says its 2-bit version ran more than 100 tool calls in Unsloth - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text and images you give it, and the card says it needs no network access - Not for: nothing named in the card ## README ````markdown # Muse Glimmer 30B GGUF Runs a 30B agent model locally that reads text and images, calls tools and retries failed calls, from Unsloth's GGUF files. For developers building local agents on consumer hardware. 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: Muse Glimmer is a 30B dense language model with a dedicated perception encoder, built for agent work on consumer hardware. The card says it was distilled from Muse Spark and runs locally with no cloud or network access. This repository holds Unsloth's GGUF files for it. The model takes text and images in and writes text out. Who it suits: It suits developers who build agents that run on their own machines, and people who want an assistant that can look at screenshots, charts and documents. ```table Industry | Who uses it | How they use it | Example Software | Agent developers | Run a local agent that calls tools and recovers from failures | Retry after a tool call returns an error Productivity | Power users | Ask about screenshots, charts and documents | Explain a chart from a screenshot Research | ML researchers | Study a distilled agent model and its quantizations | Compare Unsloth's quantized files ``` Anyone without about 14 GB of room for the 4-bit weights can skip it. Where to use it: - Unsloth, where the card says it runs with toggles for thinking - Agent scaffolds such as OpenClaw and Hermes Agent, and other orchestration patterns - Your own machine, with no cloud service - Windows, macOS or Linux, where the files download with Python ```table Use case | Who | What they get back Finish a multi-step task | Agent developers | A completed workflow built from several tool calls Write and debug code | Developers | Code and fixes inside an agent scaffold Read a screenshot or chart | Everyday users | A text explanation of what the image shows ``` How to set it up: - Python with pip, for the download - A runtime that can load GGUF files, such as Unsloth - Memory for about 13.9 GB of weights at 4-bit, as an estimate 1. Install the Hugging Face hub client. 2. Download the repository's files. 3. Load a quantization in Unsloth or another GGUF runtime. ```sh pip install -U huggingface_hub hf download unsloth/Muse-Glimmer-30B-GGUF ``` How to use it: Run the model in Unsloth and switch thinking on or off with its toggles. The card points to Unsloth's own guide for quantization analysis and step-by-step instructions. ```table Tool | What it does Unsloth | Runs the model locally, with toggles for thinking Perception encoder | Lets the model read text and images together, with up to 4,096 visual tokens per image Effort control | Picks a balance between answer quality and speed Tool calling | Calls functions with exact schemas across long workflows ``` Example: You give it a screenshot of an error message and ask it to find the cause. If a tool call fails along the way, the card says the model works out why and tries again rather than stopping. For newcomers and experts: Think of it as a local assistant that can look at pictures and use tools. Start with a plain text question in Unsloth, then try a screenshot. The language model is a dense causal transformer with 52 layers, a hidden width of 6656 and grouped-query attention with 32 query and 2 key-value heads. Attention repeats three local layers, then one global layer, with a 2048-token sliding window. RoPE applies to the local layers only. The perception encoder is a ViT-G/14 of about 1.8B parameters with 50 layers. ```flow Text and images go in The perception encoder reads the images The 30B model plans and calls tools A failed call is diagnosed and retried Text comes out ``` Good to know: - Context: 131,072 tokens or more - Languages: trained on data from more than 100 - Size: the card says about 29.6B parameters with the vision encoder, and the record lists 27.9B for these files - Tool calls: Unsloth's page says its 2-bit version ran more than 100 tool calls in Unsloth - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text and images you give it, and the card says it needs no network access - Not for: nothing named in the card ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 27.9B - Weights: about 13.9 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 692,334 - Likes: 592 - License: apache-2.0 - Based on: meta-models/Muse-Glimmer-30B - Quantized versions: 2 - Tags: transformers, gguf, unsloth, meta, image-text-to-text, conversational ## Links - Model: https://huggingface.co/unsloth/Muse-Glimmer-30B-GGUF - Files: https://huggingface.co/unsloth/Muse-Glimmer-30B-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download unsloth/Muse-Glimmer-30B-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/Muse-Glimmer-30B-GGUF on Hugging Face](https://huggingface.co/unsloth/Muse-Glimmer-30B-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 | | --- | --- | --- | --- | --- | --- | | Muse Glimmer 30B GGUF (this page) | 692,334 | 592 | 27.9B | apache-2.0 | 2026-08-10 | | [Llama-3.2-3B-Instruct-GGUF by unsloth](https://mindket.com/p/ab971aa3-d8d6-4252-8d06-8a7963b1ad29) | 243,788 | 93 | 3.2B | llama3.2 | 2025-11-08 | | [Llama-3.2-1B-Instruct-GGUF](https://mindket.com/p/bbc280ad-bc5e-44ab-bbe3-8d5a6cab1a9b) | 53,598 | 85 | 1.2B | llama3.2 | 2025-05-09 | | [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 | | [Qwen3.5-9B-GGUF](https://mindket.com/p/0f08c237-de8d-4e85-a65e-69b9b6061be0) | 1,969,901 | 1,074 | 9B | apache-2.0 | 2026-03-02 | | [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 | 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 · walrus01 · 2026-08-11](https://news.ycombinator.com/item?id=49251771): Muse Glimmer 30B unsloth GGUF format Q4, Q6, Q8 quantizations - [Bluesky · Unsloth AI · 2026-08-10](https://bsky.app/profile/unsloth.ai/post/3mspv7me4l222): Meta releases Muse Glimmer, a new 30B open model that runs on 18GB RAM. Apache 2.0 licensed and built for agentic coding, it supports vision and is the strongest agentic model for its size. Run and train the model via Unsloth. GGUF: huggingface.co/unsloth/Muse... Guide: unsloth.ai/docs/models/... - [Hacker News · Aurornis · 2026-08-10](https://news.ycombinator.com/item?id=49245534): Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows: Unsloth has quantized versions uploaded: https://huggingface.co/unsloth/Muse-Glimmer-30B-GGUF The quantized releases often change in the weeks following release as new improvements are discovered, so either use a tool that checks HuggingFace for new versions or manually check back in a few days or… - [Bluesky · Knut Jägersberg · 2026-08-10](https://bsky.app/profile/knut-jagersberg.wsocial.eu/post/3mspyokpss22e): unsloth/Muse-Glimmer-30B-GGUF Authors: Meta Superintelligence Lab Muse Glimmer is a 30-billion-parameter causal language model with a dedicated perception encoder, distilled from Muse Spark and purpose-built for autonomous agentic tasks on consumer hardware. huggingface.co/unsloth/Muse... - [Hacker News · jakswa · 2026-08-10](https://news.ycombinator.com/item?id=49243748): Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows: Q3 results: unsloth/Muse-Glimmer-30B-GGUF:UD-Q3\_K\_XL gets down to 15.6GB VRAM and full context (131k) on the 4 parallel slots. Prompt/generation speeds about the same. Overall feeling like a nicer-fitting Qwen 3.6 27B, but want to test out MTP generation speeds once I can. edit: My favorite bit of… - [Bluesky · デイリーHuggingFaceトレンド · 2026-08-14](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mszypxiysa2s): 今日のHuggingFaceトレンド unsloth/Muse-Glimmer-30B-GGUF Muse Glimmerは、Meta Superintelligence Labが開発した300億パラメータの因果言語モデルです。 消費者向けハードウェアでの自律的なエージェントタスク向けに設計されており、マルチステップ推論、ツール利用、マルチモーダル理解、故障復旧機能を備えています。 クラウドやネットワークを必要とせず、ローカル環境で動作することを目的としています。 - [Hacker News · jakswa · 2026-08-10](https://news.ycombinator.com/item?id=49243581): Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows: Another candidate for the 7900XT (20GB VRAM) I got sitting around. I pulled latest llama.cpp (targeting vulkan during build) after seeing a muse PR merged a few hours ago, and unsloth/Muse-Glimmer-30B-GGUF:UD-Q4\_K\_XL runs on my 7900XT barely (and with no MTP). Sits at 19GB VRAM w/ 4 parallel 113k… - [Bluesky · KaLiKa · 2026-08-11](https://bsky.app/profile/kalika.de/post/3msrul7ggsd2f): Muse Glimmer 30B: Metas lokales Agentenmodell in der Praxis – Einrichtung, Benchmarks, Grenzen Meta hat mit Muse Glimmer ein 30B-Agentenmodell unter Apache 2.0 veröffentlicht. So richtest du es lokal ein, welche Hardware du... https://www.kalika.de/posts/unsloth-muse-glimmer-30b-gguf-hugging-face/ ## 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: 2026-08-10 - First published: 2026-08-10 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open unsloth/Muse-Glimmer-30B-GGUF on Hugging Face](https://huggingface.co/unsloth/Muse-Glimmer-30B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/31b81c35-03e2-4c43-ad62-fe42f4b51f11) - [Its page on Mindket](https://mindket.com/p/31b81c35-03e2-4c43-ad62-fe42f4b51f11)